{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Iterating over multiple refutation tests\n",
"The objective of this notebook is to compare the ability of refuters to detect the problems in a given set of estimators.\n",
"Note:\n",
"This notebook makes use of the optional dependencies:\n",
"- pygraphviz\n",
"- causalml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from dowhy.datasets import linear_dataset\n",
"from dowhy import CausalModel\n",
"import causalml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspection Parameters\n",
"These parameters give us the option of inspecting the intermediate steps to sanity check the steps performed"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"inspect_datasets = True\n",
"inspect_models = True\n",
"inspect_identified_estimands = True\n",
"inspect_estimates = True\n",
"inspect_refutations = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimator List\n",
"We pass a list of strings, corresponding to the estimators of interest"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"estimator_list = [\"backdoor.propensity_score_matching\", \"backdoor.propensity_score_weighting\", \"backdoor.causalml.inference.meta.LRSRegressor\"]\n",
"method_params= [ None, None, { \"init_params\":{} } ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refuter List\n",
"A list of strings, corresponding to each refuter we wish to run"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"refuter_list = [\"bootstrap_refuter\", \"data_subset_refuter\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the Datasets"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Parameters for creating the Dataset\n",
"TREATMENT_IS_BINARY = True\n",
"BETA = 10\n",
"NUM_SAMPLES = 5000\n",
"NUM_CONFOUNDERS = 5\n",
"NUM_INSTRUMENTS = 3\n",
"NUM_EFFECT_MODIFIERS = 2\n",
"\n",
"# Creating a Linear Dataset with the given parameters\n",
"linear_data = linear_dataset(\n",
" beta = BETA,\n",
" num_common_causes = NUM_CONFOUNDERS,\n",
" num_instruments = NUM_INSTRUMENTS,\n",
" num_effect_modifiers = NUM_EFFECT_MODIFIERS,\n",
" num_samples = NUM_SAMPLES,\n",
" treatment_is_binary = True\n",
" )\n",
"# Other datasets come here \n",
"\n",
"\n",
"# Append them together in an array\n",
"datasets = [linear_data]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspect Data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"####### Dataset 1###########################################################################################\n",
" X0 X1 Z0 Z1 Z2 W0 W1 W2 \\\n",
"0 -1.372310 -0.633368 0.0 0.353144 0.0 -0.089772 -0.838580 0.284335 \n",
"1 0.713931 0.802709 0.0 0.653470 1.0 -1.083905 -1.160895 0.591947 \n",
"2 -0.384362 -0.451062 0.0 0.341027 1.0 -1.344159 -0.966449 -0.181318 \n",
"3 1.515319 -1.363540 0.0 0.835704 1.0 0.342380 -1.004874 1.552707 \n",
"4 0.368875 -0.829938 0.0 0.071521 0.0 -0.128446 -1.994744 0.519826 \n",
"\n",
" W3 W4 v0 y \n",
"0 1.223258 -1.124599 True 2.034000 \n",
"1 1.779737 -0.612423 True 11.125450 \n",
"2 -0.290884 -1.743305 False -9.777346 \n",
"3 1.042000 -2.330312 True 10.343818 \n",
"4 0.479455 -1.345510 False -4.253140 \n",
"#############################################################################################################\n"
]
}
],
"source": [
"dataset_num = 1\n",
"if inspect_datasets is True:\n",
" for data in datasets:\n",
" print(\"####### Dataset {}###########################################################################################\".format(dataset_num))\n",
" print(data['df'].head())\n",
" print(\"#############################################################################################################\")\n",
" dataset_num += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create the CausalModels"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_model:Model to find the causal effect of treatment ['v0'] on outcome ['y']\n"
]
}
],
"source": [
"models = []\n",
"for data in datasets:\n",
" model = CausalModel(\n",
" data = data['df'],\n",
" treatment = data['treatment_name'],\n",
" outcome = data['outcome_name'],\n",
" graph = data['gml_graph']\n",
" )\n",
" models.append(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspect Models"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"####### Model 1#############################################################################################\n",
"Common Causes: ['W4', 'W2', 'W1', 'W3', 'W0', 'Unobserved Confounders']\n",
"Effect Modifiers: ['X0', 'X1']\n",
"Instruments: ['Z2', 'Z1', 'Z0']\n",
"Outcome: ['y']\n",
"Treatment: ['v0']\n",
"#############################################################################################################\n"
]
}
],
"source": [
"model_num = 1\n",
"if inspect_models is True:\n",
" for model in models:\n",
" print(\"####### Model {}#############################################################################################\".format(model_num))\n",
" print(\"Common Causes:\",model._common_causes)\n",
" print(\"Effect Modifiers:\",model._effect_modifiers)\n",
" print(\"Instruments:\",model._instruments)\n",
" print(\"Outcome:\",model._outcome)\n",
" print(\"Treatment:\",model._treatment)\n",
" print(\"#############################################################################################################\")\n",
" model_num += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Identify Effect"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
"INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
"INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:['Z2', 'Z1', 'Z0']\n",
"INFO:dowhy.causal_identifier:Frontdoor variables for treatment and outcome:[]\n"
]
}
],
"source": [
"identified_estimands = []\n",
"for model in models:\n",
" identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
" identified_estimands.append(identified_estimand)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Identified Estimands"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"####### Identified Estimand 1#####################################################################################\n",
"Estimand type: nonparametric-ate\n",
"\n",
"### Estimand : 1\n",
"Estimand name: backdoor1\n",
"Estimand expression:\n",
" d \n",
"─────(Expectation(y|W4,W2,W1,W3,W0))\n",
"d[v₀] \n",
"Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W1,W3,W0,U) = P(y|v0,W4,W2,W1,W3,W0)\n",
"\n",
"### Estimand : 2\n",
"Estimand name: backdoor2\n",
"Estimand expression:\n",
" d \n",
"─────(Expectation(y|W4,W2,W1,W3,W0,X1))\n",
"d[v₀] \n",
"Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W1,W3,W0,X1,U) = P(y|v0,W4,W2,W1,W3,W0,X1)\n",
"\n",
"### Estimand : 3\n",
"Estimand name: backdoor3\n",
"Estimand expression:\n",
" d \n",
"─────(Expectation(y|W4,W2,W1,W3,W0,X0))\n",
"d[v₀] \n",
"Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W1,W3,W0,X0,U) = P(y|v0,W4,W2,W1,W3,W0,X0)\n",
"\n",
"### Estimand : 4\n",
"Estimand name: backdoor4 (Default)\n",
"Estimand expression:\n",
" d \n",
"─────(Expectation(y|W4,W2,W1,W3,W0,X1,X0))\n",
"d[v₀] \n",
"Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W4,W2,W1,W3,W0,X1,X0,U) = P(y|v0,W4,W2,W1,W3,W0,X1,X0)\n",
"\n",
"### Estimand : 5\n",
"Estimand name: iv\n",
"Estimand expression:\n",
"Expectation(Derivative(y, [Z2, Z1, Z0])*Derivative([v0], [Z2, Z1, Z0])**(-1))\n",
"Estimand assumption 1, As-if-random: If U→→y then ¬(U →→{Z2,Z1,Z0})\n",
"Estimand assumption 2, Exclusion: If we remove {Z2,Z1,Z0}→{v0}, then ¬({Z2,Z1,Z0}→y)\n",
"\n",
"### Estimand : 6\n",
"Estimand name: frontdoor\n",
"No such variable found!\n",
"\n",
"###################################################################################################################\n"
]
}
],
"source": [
"estimand_count = 1\n",
"for estimand in identified_estimands:\n",
" print(\"####### Identified Estimand {}#####################################################################################\".format(estimand_count))\n",
" print(estimand)\n",
" print(\"###################################################################################################################\")\n",
" estimand_count += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimate Effect"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"/home/amit/py-envs/env3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" return f(**kwargs)\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"/home/amit/py-envs/env3.8/lib/python3.8/site-packages/sklearn/utils/validation.py:72: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
" return f(**kwargs)\n",
"The sklearn.utils.testing module is deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0464\n",
"INFO:causalml: RMSE (Treatment): 0.7089\n",
"INFO:causalml: sMAPE (Control): 0.5415\n",
"INFO:causalml: sMAPE (Treatment): 0.1450\n",
"INFO:causalml: Gini (Control): 0.7392\n",
"INFO:causalml: Gini (Treatment): 0.9950\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"0 -1.124599 0.284335 -0.838580 1.223258 -0.089772 -0.633368 -1.372310 \n",
"1 -0.612423 0.591947 -1.160895 1.779737 -1.083905 0.802709 0.713931 \n",
"2 -1.743305 -0.181318 -0.966449 -0.290884 -1.344159 -0.451062 -0.384362 \n",
"3 -2.330312 1.552707 -1.004874 1.042000 0.342380 -1.363540 1.515319 \n",
"4 -1.345510 0.519826 -1.994744 0.479455 -0.128446 -0.829938 0.368875 \n",
".. ... ... ... ... ... ... ... \n",
"995 -2.515948 0.660984 -1.169142 -2.272146 -1.210642 -0.284190 -0.127452 \n",
"996 -2.943305 1.765768 -0.453093 1.811460 1.207236 1.056658 2.059214 \n",
"997 -0.903931 -0.733567 -0.631920 2.074003 -1.104834 -0.227741 -0.262214 \n",
"998 -0.107884 0.866378 -1.644002 -0.046340 -1.318515 0.888535 1.662692 \n",
"999 -1.664178 0.364437 -0.018848 0.440983 -1.575637 -0.289049 -0.646924 \n",
"\n",
" X0 X1 \n",
"0 -1.372310 -0.633368 \n",
"1 0.713931 0.802709 \n",
"2 -0.384362 -0.451062 \n",
"3 1.515319 -1.363540 \n",
"4 0.368875 -0.829938 \n",
".. ... ... \n",
"995 -0.127452 -0.284190 \n",
"996 2.059214 1.056658 \n",
"997 -0.262214 -0.227741 \n",
"998 1.662692 0.888535 \n",
"999 -0.646924 -0.289049 \n",
"\n",
"[1000 rows x 9 columns], 'y': 0 2.034000\n",
"1 11.125450\n",
"2 -9.777346\n",
"3 10.343818\n",
"4 -4.253140\n",
" ... \n",
"995 -13.283829\n",
"996 17.211685\n",
"997 4.590505\n",
"998 13.321122\n",
"999 -8.100697\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 0 True\n",
"1 True\n",
"2 False\n",
"3 True\n",
"4 False\n",
" ... \n",
"995 False\n",
"996 True\n",
"997 True\n",
"998 True\n",
"999 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
}
],
"source": [
"estimate_list = []\n",
"for i in range(len(identified_estimands)):\n",
" for j in range(len(estimator_list)):\n",
" estimate = model.estimate_effect(\n",
" identified_estimands[i],\n",
" method_name=estimator_list[j],\n",
" method_params=method_params[j]\n",
" )\n",
" estimate_list.append(estimate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimate Values"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"####### Estimand 1#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"<class 'dowhy.causal_estimators.propensity_score_matching_estimator.PropensityScoreMatchingEstimator'>\n",
"\n",
"*** Causal Estimate ***\n",
"\n",
"## Identified estimand\n",
"Estimand type: nonparametric-ate\n",
"\n",
"## Realized estimand\n",
"b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"Target units: ate\n",
"\n",
"## Estimate\n",
"Mean value: 12.89367255934708\n",
"\n",
"########################################################################################################\n",
"\n",
"####### Estimand 2#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"<class 'dowhy.causal_estimators.propensity_score_weighting_estimator.PropensityScoreWeightingEstimator'>\n",
"\n",
"*** Causal Estimate ***\n",
"\n",
"## Identified estimand\n",
"Estimand type: nonparametric-ate\n",
"\n",
"## Realized estimand\n",
"b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"Target units: ate\n",
"\n",
"## Estimate\n",
"Mean value: 12.075122341243496\n",
"\n",
"########################################################################################################\n",
"\n",
"####### Estimand 3#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"<class 'dowhy.causal_estimators.causalml.Causalml'>\n",
"\n",
"*** Causal Estimate ***\n",
"\n",
"## Identified estimand\n",
"Estimand type: nonparametric-ate\n",
"\n",
"## Realized estimand\n",
"b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"Target units: ate\n",
"\n",
"## Estimate\n",
"Mean value: [11.29290641]\n",
"\n",
"########################################################################################################\n",
"\n"
]
}
],
"source": [
"estimand_count = 1\n",
"if inspect_estimates is True:\n",
" for estimand in estimate_list:\n",
" print(\"####### Estimand {}#######################################################################################\".format(estimand_count))\n",
" print(\"*** Class Name ***\")\n",
" print()\n",
" print(estimand.params['estimator_class'])\n",
" print()\n",
" print(estimand)\n",
" print(\"########################################################################################################\")\n",
" print()\n",
" estimand_count += 1\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refute Estimate"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_refuters.bootstrap_refuter:All variables required: Running bootstrap adding noise to confounders, instrumental variables and effect modifiers.\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:INFO: The chosen variables are: W4,W2,W1,W3,W0,X1,X0,Z2,Z1,Z0,X0,X1\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:Refutation over 100 simulated datasets of size 1000 each\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:Making use of Bootstrap as we have more than 100 examples.\n",
" Note: The greater the number of examples, the more accurate are the confidence estimates\n",
"INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.8 simulated datasets of size 800.0 each\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Matching Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_refuters.data_subset_refuter:Making use of Bootstrap as we have more than 100 examples.\n",
" Note: The greater the number of examples, the more accurate are the confidence estimates\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:All variables required: Running bootstrap adding noise to confounders, instrumental variables and effect modifiers.\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:INFO: The chosen variables are: W4,W2,W1,W3,W0,X1,X0,Z2,Z1,Z0,X0,X1\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:Refutation over 100 simulated datasets of size 1000 each\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:Making use of Bootstrap as we have more than 100 examples.\n",
" Note: The greater the number of examples, the more accurate are the confidence estimates\n",
"INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.8 simulated datasets of size 800.0 each\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_estimator:INFO: Using Propensity Score Weighting Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
"INFO:dowhy.causal_refuters.data_subset_refuter:Making use of Bootstrap as we have more than 100 examples.\n",
" Note: The greater the number of examples, the more accurate are the confidence estimates\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:All variables required: Running bootstrap adding noise to confounders, instrumental variables and effect modifiers.\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:INFO: The chosen variables are: W4,W2,W1,W3,W0,X1,X0,Z2,Z1,Z0,X0,X1\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:Refutation over 100 simulated datasets of size 1000 each\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9200\n",
"INFO:causalml: RMSE (Treatment): 0.9412\n",
"INFO:causalml: sMAPE (Control): 0.4925\n",
"INFO:causalml: sMAPE (Treatment): 0.1825\n",
"INFO:causalml: Gini (Control): 0.7570\n",
"INFO:causalml: Gini (Treatment): 0.9895\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"283 -2.374933 0.728965 -0.368648 -0.606642 1.295353 -1.524890 0.835089 \n",
"817 0.427994 1.189968 -0.603155 -0.053104 0.380095 -0.564310 1.043702 \n",
"433 -1.848292 1.925231 -0.746389 0.838242 0.454850 -1.782858 0.315274 \n",
"518 -1.612950 -0.302839 -2.806152 2.278601 0.851459 -0.216496 -0.266022 \n",
"760 -1.603621 0.332341 0.905154 -1.867772 -0.335681 -1.016100 0.900808 \n",
".. ... ... ... ... ... ... ... \n",
"660 -1.355739 1.511612 0.382854 -1.674767 1.278216 0.055724 0.586001 \n",
"983 -1.218331 1.260066 -0.595561 -0.288691 0.395709 -0.170658 -0.066706 \n",
"98 -0.845113 -0.187431 -2.262896 1.378585 -0.644947 1.959020 0.299983 \n",
"972 -0.081695 0.594733 -0.969979 1.791205 1.145891 -1.370514 -1.418567 \n",
"923 -1.936169 2.043045 -0.395622 2.093163 -0.270379 -1.179542 1.467835 \n",
"\n",
" X0 X1 \n",
"283 0.835089 -1.524890 \n",
"817 1.043702 -0.564310 \n",
"433 0.315274 -1.782858 \n",
"518 -0.266022 -0.216496 \n",
"760 0.900808 -1.016100 \n",
".. ... ... \n",
"660 0.586001 0.055724 \n",
"983 -0.066706 -0.170658 \n",
"98 0.299983 1.959020 \n",
"972 -1.418567 -1.370514 \n",
"923 1.467835 -1.179542 \n",
"\n",
"[1000 rows x 9 columns], 'y': 283 7.081134\n",
"817 17.203287\n",
"433 7.710126\n",
"518 -0.840870\n",
"760 -7.109575\n",
" ... \n",
"660 11.491007\n",
"983 7.361146\n",
"98 -3.658929\n",
"972 10.333966\n",
"923 11.892142\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 283 True\n",
"817 True\n",
"433 True\n",
"518 False\n",
"760 False\n",
" ... \n",
"660 True\n",
"983 True\n",
"98 False\n",
"972 True\n",
"923 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8989\n",
"INFO:causalml: RMSE (Treatment): 0.9262\n",
"INFO:causalml: sMAPE (Control): 0.5243\n",
"INFO:causalml: sMAPE (Treatment): 0.1639\n",
"INFO:causalml: Gini (Control): 0.7420\n",
"INFO:causalml: Gini (Treatment): 0.9882\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"583 -0.908339 -0.943344 -1.576363 -0.129984 0.644029 -0.283406 1.047899 \n",
"894 -0.035766 0.592029 -2.679506 0.298057 -0.918355 -0.136446 1.547318 \n",
"441 0.082396 1.734068 -1.234019 -1.393834 -0.012471 0.176847 2.271376 \n",
"19 -0.501483 -0.141436 -0.783533 2.196045 -1.062579 -1.248909 0.217419 \n",
"635 -0.328230 2.032342 -1.397294 1.604553 -0.906293 1.272577 1.348827 \n",
".. ... ... ... ... ... ... ... \n",
"681 -0.659808 1.173054 0.680265 -1.119354 1.271902 -0.028103 1.906201 \n",
"999 -1.548070 0.488709 0.070530 0.535239 -1.520216 -0.223398 -0.643285 \n",
"601 0.197730 -0.348816 -2.289017 0.706303 -0.436227 -0.869317 0.393254 \n",
"922 -0.957356 2.303012 1.357823 -2.150545 -0.526943 0.693399 -0.542549 \n",
"85 -1.507585 1.395092 -0.629172 -0.127097 -1.095902 -2.763081 -0.159951 \n",
"\n",
" X0 X1 \n",
"583 1.047899 -0.283406 \n",
"894 1.547318 -0.136446 \n",
"441 2.271376 0.176847 \n",
"19 0.217419 -1.248909 \n",
"635 1.348827 1.272577 \n",
".. ... ... \n",
"681 1.906201 -0.028103 \n",
"999 -0.643285 -0.223398 \n",
"601 0.393254 -0.869317 \n",
"922 -0.542549 0.693399 \n",
"85 -0.159951 -2.763081 \n",
"\n",
"[1000 rows x 9 columns], 'y': 583 -2.819370\n",
"894 -2.776312\n",
"441 18.248395\n",
"19 6.700542\n",
"635 15.613398\n",
" ... \n",
"681 18.090454\n",
"999 -8.100697\n",
"601 -1.707354\n",
"922 5.614218\n",
"85 -0.384362\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 583 False\n",
"894 False\n",
"441 True\n",
"19 True\n",
"635 True\n",
" ... \n",
"681 True\n",
"999 False\n",
"601 False\n",
"922 True\n",
"85 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0143\n",
"INFO:causalml: RMSE (Treatment): 0.9978\n",
"INFO:causalml: sMAPE (Control): 0.5401\n",
"INFO:causalml: sMAPE (Treatment): 0.1764\n",
"INFO:causalml: Gini (Control): 0.7350\n",
"INFO:causalml: Gini (Treatment): 0.9868\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"46 -0.811883 1.026880 -0.632654 0.186775 -1.837921 0.413697 1.853316 \n",
"438 -1.340902 0.995483 -0.161424 0.746464 -0.289784 -0.655573 0.779645 \n",
"658 -0.748875 0.200811 -2.089700 0.715665 -0.392931 -0.898996 0.666150 \n",
"789 -1.509320 -0.240769 1.557885 -0.108347 -0.045226 0.412988 1.082957 \n",
"702 1.732221 1.344143 0.218604 -0.185893 1.519994 0.269678 0.498933 \n",
".. ... ... ... ... ... ... ... \n",
"351 -0.831764 0.623752 -0.471018 0.970281 -0.177779 -1.707145 0.027269 \n",
"513 -0.705018 1.506675 -0.629371 2.376489 3.098338 -0.308193 0.523668 \n",
"784 -1.254205 0.728475 -1.482794 -0.023014 -0.163497 2.000347 1.558882 \n",
"631 0.369918 2.644170 -0.867136 1.586151 -0.202800 -0.997252 0.248708 \n",
"469 -1.874152 0.163053 0.352744 -2.044516 -1.017211 -1.678673 1.633251 \n",
"\n",
" X0 X1 \n",
"46 1.853316 0.413697 \n",
"438 0.779645 -0.655573 \n",
"658 0.666150 -0.898996 \n",
"789 1.082957 0.412988 \n",
"702 0.498933 0.269678 \n",
".. ... ... \n",
"351 0.027269 -1.707145 \n",
"513 0.523668 -0.308193 \n",
"784 1.558882 2.000347 \n",
"631 0.248708 -0.997252 \n",
"469 1.633251 -1.678673 \n",
"\n",
"[1000 rows x 9 columns], 'y': 46 -6.116730\n",
"438 8.571639\n",
"658 8.483868\n",
"789 11.103887\n",
"702 23.877017\n",
" ... \n",
"351 7.214058\n",
"513 22.411994\n",
"784 14.827481\n",
"631 14.220450\n",
"469 4.435916\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 46 False\n",
"438 True\n",
"658 True\n",
"789 True\n",
"702 True\n",
" ... \n",
"351 True\n",
"513 True\n",
"784 True\n",
"631 True\n",
"469 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8926\n",
"INFO:causalml: RMSE (Treatment): 0.9835\n",
"INFO:causalml: sMAPE (Control): 0.5059\n",
"INFO:causalml: sMAPE (Treatment): 0.1802\n",
"INFO:causalml: Gini (Control): 0.7225\n",
"INFO:causalml: Gini (Treatment): 0.9871\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"110 -1.701609 2.531055 -0.293499 1.383457 -0.385890 1.021554 0.634298 \n",
"973 0.497997 0.870619 -1.399658 0.868762 1.005668 -1.759774 -1.487125 \n",
"253 0.008862 0.472530 -0.278656 1.597479 -1.854917 0.218323 1.158774 \n",
"774 -0.690558 0.152730 0.466841 -0.784936 0.262644 -1.272747 0.138279 \n",
"25 -0.555348 1.680622 -0.274376 0.085447 -1.937412 -0.524363 1.456005 \n",
".. ... ... ... ... ... ... ... \n",
"433 -1.759517 1.932299 -0.786689 0.690382 0.385271 -1.815584 0.191754 \n",
"517 -1.412816 0.065002 0.010819 1.228889 -1.019312 -0.737759 1.051565 \n",
"241 -0.690211 2.358994 -0.801637 1.877993 0.244108 -2.726661 -0.526157 \n",
"840 0.825515 0.764723 0.303671 0.981057 -0.217922 -0.091909 0.598068 \n",
"808 -0.535757 0.997121 -0.516028 -0.574787 0.787579 -1.200082 1.220149 \n",
"\n",
" X0 X1 \n",
"110 0.634298 1.021554 \n",
"973 -1.487125 -1.759774 \n",
"253 1.158774 0.218323 \n",
"774 0.138279 -1.272747 \n",
"25 1.456005 -0.524363 \n",
".. ... ... \n",
"433 0.191754 -1.815584 \n",
"517 1.051565 -0.737759 \n",
"241 -0.526157 -2.726661 \n",
"840 0.598068 -0.091909 \n",
"808 1.220149 -1.200082 \n",
"\n",
"[1000 rows x 9 columns], 'y': 110 11.934711\n",
"973 8.500263\n",
"253 11.727481\n",
"774 6.080336\n",
"25 8.623876\n",
" ... \n",
"433 7.710126\n",
"517 -4.981074\n",
"241 7.793042\n",
"840 15.748095\n",
"808 14.588358\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 110 True\n",
"973 True\n",
"253 True\n",
"774 True\n",
"25 True\n",
" ... \n",
"433 True\n",
"517 False\n",
"241 True\n",
"840 True\n",
"808 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"553 -0.320702 2.050546 0.848221 0.065701 0.516036 -0.115430 -0.448765 \n",
"263 -0.436130 1.311757 0.801788 0.976110 -1.434723 1.339524 -0.740828 \n",
"318 -0.323014 -0.607556 -0.157762 0.004904 1.536271 -0.407724 -0.367022 \n",
"106 -1.558304 1.265530 0.118377 -0.333680 -1.996853 -1.773391 1.138244 \n",
"926 1.123598 0.627467 -0.761817 1.094964 -0.861427 -0.395120 0.162772 \n",
".. ... ... ... ... ... ... ... \n",
"354 -2.470658 2.299726 -0.513947 -0.533862 1.235853 0.471081 0.403984 \n",
"665 -2.051712 0.111099 -0.161581 -0.859187 0.133294 -0.468604 -0.057014 \n",
"43 -0.942660 1.261453 0.077870 0.553020 -1.012981 -0.392755 1.398809 \n",
"805 -2.534599 -0.173973 -0.855050 -0.359868 0.531906 -2.824743 0.317926 \n",
"873 -0.107712 -0.360830 0.754699 0.645171 -1.063732 -2.205695 0.040772 \n",
"\n",
" X0 X1 \n",
"553 -0.448765 -0.115430 \n",
"263 -0.740828 1.339524 \n",
"318 -0.367022 -0.407724 \n",
"106 1.138244 -1.773391 \n",
"926 0.162772 -0.395120 \n",
".. ... ... \n",
"354 0.403984 0.471081 \n",
"665 -0.057014 -0.468604 \n",
"43 1.398809 -0.392755 \n",
"805 0.317926 -2.824743 \n",
"873 0.040772 -2.205695 \n",
"\n",
"[1000 rows x 9 columns], 'y': 553 11.754777\n",
"263 5.315574\n",
"318 10.068543\n",
"106 -8.631314\n",
"926 13.032000\n",
" ... \n",
"354 9.572774\n",
"665 2.331887\n",
"43 11.590880\n",
"805 -0.721618\n",
"873 4.499265\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 553 True\n",
"263 True\n",
"318 True\n",
"106 False\n",
"926 True\n",
" ... \n",
"354 True\n",
"665 True\n",
"43 True\n",
"805 True\n",
"873 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1093\n",
"INFO:causalml: RMSE (Treatment): 1.0340\n",
"INFO:causalml: sMAPE (Control): 0.5506\n",
"INFO:causalml: sMAPE (Treatment): 0.1787\n",
"INFO:causalml: Gini (Control): 0.7321\n",
"INFO:causalml: Gini (Treatment): 0.9865\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9188\n",
"INFO:causalml: RMSE (Treatment): 0.9924\n",
"INFO:causalml: sMAPE (Control): 0.5366\n",
"INFO:causalml: sMAPE (Treatment): 0.1953\n",
"INFO:causalml: Gini (Control): 0.7756\n",
"INFO:causalml: Gini (Treatment): 0.9901\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"615 -1.406038 0.572876 -0.698405 1.326372 -2.542542 -1.169138 2.178432 \n",
"124 -1.122468 2.232510 0.453520 0.286994 0.573583 -0.104966 -0.616633 \n",
"671 -1.835079 1.952091 0.539765 0.587736 -1.553163 -0.139247 -0.565739 \n",
"354 -2.414675 2.195083 -0.552665 -0.286460 1.364807 0.672503 0.211691 \n",
"971 -1.008125 0.813609 -1.174516 0.831382 0.600010 1.860670 1.222419 \n",
".. ... ... ... ... ... ... ... \n",
"140 -0.076572 -0.183218 -1.087970 1.721934 -1.409678 0.371262 0.084339 \n",
"441 0.164827 1.727422 -1.133955 -1.106994 -0.174687 0.462557 2.301826 \n",
"944 -1.317575 1.334951 0.214215 -0.090740 0.344393 -1.939722 0.185537 \n",
"504 -1.936715 2.198026 0.936412 -0.674665 -1.200826 -0.802310 0.450366 \n",
"579 -0.367602 0.368260 0.366198 0.370464 -1.268264 -1.013708 -0.296588 \n",
"\n",
" X0 X1 \n",
"615 2.178432 -1.169138 \n",
"124 -0.616633 -0.104966 \n",
"671 -0.565739 -0.139247 \n",
"354 0.211691 0.672503 \n",
"971 1.222419 1.860670 \n",
".. ... ... \n",
"140 0.084339 0.371262 \n",
"441 2.301826 0.462557 \n",
"944 0.185537 -1.939722 \n",
"504 0.450366 -0.802310 \n",
"579 -0.296588 -1.013708 \n",
"\n",
"[1000 rows x 9 columns], 'y': 615 -8.516564\n",
"124 10.147994\n",
"671 -6.150284\n",
"354 9.572774\n",
"971 16.046613\n",
" ... \n",
"140 -3.623800\n",
"441 18.248395\n",
"944 5.893961\n",
"504 4.307409\n",
"579 3.518524\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 615 False\n",
"124 True\n",
"671 False\n",
"354 True\n",
"971 True\n",
" ... \n",
"140 False\n",
"441 True\n",
"944 True\n",
"504 True\n",
"579 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1321\n",
"INFO:causalml: RMSE (Treatment): 0.9716\n",
"INFO:causalml: sMAPE (Control): 0.5822\n",
"INFO:causalml: sMAPE (Treatment): 0.1791\n",
"INFO:causalml: Gini (Control): 0.6922\n",
"INFO:causalml: Gini (Treatment): 0.9885\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"483 -1.202986 0.901468 -0.302848 0.668258 -1.940498 0.222960 0.612932 \n",
"778 -2.032255 -0.391939 0.024425 0.116800 0.320421 -1.642899 0.789443 \n",
"364 -1.123898 1.426482 1.582027 -0.519401 -1.549683 -0.385146 0.951193 \n",
"437 -1.765053 2.519452 0.710983 0.111217 -1.534482 -1.368957 -0.086755 \n",
"518 -1.577778 -0.426756 -2.600338 2.425733 0.913174 0.100396 -0.440147 \n",
".. ... ... ... ... ... ... ... \n",
"414 -0.773099 0.363187 -2.923567 0.521046 -1.501182 -1.106220 2.408646 \n",
"419 -0.132182 -0.589329 0.304130 0.361943 -0.778306 1.341490 0.877095 \n",
"971 -1.156021 0.689227 -1.100481 1.059006 0.528747 1.847563 1.274255 \n",
"855 -1.604108 2.748376 0.301092 -0.817147 -0.256705 -0.736611 0.902897 \n",
"971 -1.047919 0.711559 -1.179255 1.142261 0.330883 1.845635 1.100617 \n",
"\n",
" X0 X1 \n",
"483 0.612932 0.222960 \n",
"778 0.789443 -1.642899 \n",
"364 0.951193 -0.385146 \n",
"437 -0.086755 -1.368957 \n",
"518 -0.440147 0.100396 \n",
".. ... ... \n",
"414 2.408646 -1.106220 \n",
"419 0.877095 1.341490 \n",
"971 1.274255 1.847563 \n",
"855 0.902897 -0.736611 \n",
"971 1.100617 1.845635 \n",
"\n",
"[1000 rows x 9 columns], 'y': 483 5.198246\n",
"778 6.019405\n",
"364 7.693260\n",
"437 1.751543\n",
"518 -0.840870\n",
" ... \n",
"414 -6.908901\n",
"419 12.060932\n",
"971 16.046613\n",
"855 8.939223\n",
"971 16.046613\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 483 True\n",
"778 True\n",
"364 True\n",
"437 True\n",
"518 False\n",
" ... \n",
"414 False\n",
"419 True\n",
"971 True\n",
"855 True\n",
"971 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9750\n",
"INFO:causalml: RMSE (Treatment): 0.9816\n",
"INFO:causalml: sMAPE (Control): 0.4629\n",
"INFO:causalml: sMAPE (Treatment): 0.1647\n",
"INFO:causalml: Gini (Control): 0.7145\n",
"INFO:causalml: Gini (Treatment): 0.9878\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"257 -2.430668 2.899099 -1.685012 0.871124 -0.830481 0.568640 -0.719302 \n",
"190 -1.027333 0.125718 -0.288925 0.143558 -1.665370 -1.891319 1.401234 \n",
"608 0.582419 2.904835 -1.679491 -1.433618 0.523356 1.427560 -0.365038 \n",
"136 -1.844962 -0.175424 -1.127409 1.046034 -0.143139 0.929437 -0.113900 \n",
"698 -1.136466 1.658704 0.342969 1.270983 -1.275504 0.318959 0.063527 \n",
".. ... ... ... ... ... ... ... \n",
"2 -1.717141 -0.110696 -0.851043 -0.159615 -1.330690 -0.575017 0.179750 \n",
"525 -1.122974 0.528479 -0.843057 0.862124 0.326137 -0.908267 0.675998 \n",
"818 -2.224628 1.267784 -0.121270 0.339637 -2.509032 -0.733553 0.969607 \n",
"772 -1.528754 0.577981 -1.230741 1.515439 -0.409506 -1.244127 -0.229884 \n",
"725 -1.062839 1.197021 -2.332341 1.110674 -0.732020 1.117293 -1.286959 \n",
"\n",
" X0 X1 \n",
"257 -0.719302 0.568640 \n",
"190 1.401234 -1.891319 \n",
"608 -0.365038 1.427560 \n",
"136 -0.113900 0.929437 \n",
"698 0.063527 0.318959 \n",
".. ... ... \n",
"2 0.179750 -0.575017 \n",
"525 0.675998 -0.908267 \n",
"818 0.969607 -0.733553 \n",
"772 -0.229884 -1.244127 \n",
"725 -1.286959 1.117293 \n",
"\n",
"[1000 rows x 9 columns], 'y': 257 2.568588\n",
"190 3.713965\n",
"608 13.248344\n",
"136 -4.977571\n",
"698 7.381116\n",
" ... \n",
"2 -9.777346\n",
"525 9.406466\n",
"818 -11.770683\n",
"772 3.250030\n",
"725 -3.118501\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 257 True\n",
"190 True\n",
"608 True\n",
"136 False\n",
"698 True\n",
" ... \n",
"2 False\n",
"525 True\n",
"818 False\n",
"772 True\n",
"725 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0590\n",
"INFO:causalml: RMSE (Treatment): 1.0471\n",
"INFO:causalml: sMAPE (Control): 0.5733\n",
"INFO:causalml: sMAPE (Treatment): 0.1911\n",
"INFO:causalml: Gini (Control): 0.7229\n",
"INFO:causalml: Gini (Treatment): 0.9862\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"277 -0.818334 0.467892 0.008818 -0.768910 -0.762255 -2.037111 -0.267297 \n",
"616 -1.801324 0.465489 1.310050 0.431569 -0.724100 0.078453 -0.281507 \n",
"251 -2.045885 -0.672299 0.461639 1.102677 -0.212650 -0.728915 0.412898 \n",
"779 -1.293648 0.585981 0.057450 0.159790 0.220197 -0.968476 -0.039945 \n",
"891 -0.173672 -0.339079 -1.162503 1.699004 1.016864 0.402539 1.951243 \n",
".. ... ... ... ... ... ... ... \n",
"220 0.682316 1.258320 -0.491097 0.533450 -1.171028 -1.744354 0.764456 \n",
"880 -1.819713 1.795421 2.289274 -0.412771 0.834959 -0.625579 0.310392 \n",
"600 -2.029851 0.999468 -0.650238 0.199650 -1.522578 -0.197616 -0.487230 \n",
"85 -1.689351 1.444803 -0.346028 -0.249449 -0.882348 -2.851097 -0.324837 \n",
"982 -0.727784 0.731025 -0.402121 -0.987436 0.733502 -1.450469 1.225178 \n",
"\n",
" X0 X1 \n",
"277 -0.267297 -2.037111 \n",
"616 -0.281507 0.078453 \n",
"251 0.412898 -0.728915 \n",
"779 -0.039945 -0.968476 \n",
"891 1.951243 0.402539 \n",
".. ... ... \n",
"220 0.764456 -1.744354 \n",
"880 0.310392 -0.625579 \n",
"600 -0.487230 -0.197616 \n",
"85 -0.324837 -2.851097 \n",
"982 1.225178 -1.450469 \n",
"\n",
"[1000 rows x 9 columns], 'y': 277 1.863805\n",
"616 4.604313\n",
"251 5.436045\n",
"779 7.001850\n",
"891 20.166272\n",
" ... \n",
"220 10.878908\n",
"880 10.445032\n",
"600 -2.038553\n",
"85 -0.384362\n",
"982 12.003112\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 277 True\n",
"616 True\n",
"251 True\n",
"779 True\n",
"891 True\n",
" ... \n",
"220 True\n",
"880 True\n",
"600 True\n",
"85 True\n",
"982 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.6412\n",
"INFO:causalml: RMSE (Treatment): 0.8491\n",
"INFO:causalml: sMAPE (Control): 0.4837\n",
"INFO:causalml: sMAPE (Treatment): 0.1494\n",
"INFO:causalml: Gini (Control): 0.7786\n",
"INFO:causalml: Gini (Treatment): 0.9905\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"241 -0.845996 2.158548 -0.696759 1.834804 0.107037 -2.852733 -0.048085 \n",
"896 -2.469059 -0.770860 1.142546 0.461676 -1.314687 -0.322351 -1.475853 \n",
"723 -0.193634 1.302947 0.686441 0.892449 -0.623708 -0.382725 0.546637 \n",
"758 -0.229260 0.153205 -0.797357 0.977691 -0.149110 -1.096648 1.138511 \n",
"649 -2.097121 1.286213 -0.793670 1.771977 -0.505928 -0.172366 1.686361 \n",
".. ... ... ... ... ... ... ... \n",
"177 -1.890133 0.349791 2.039631 0.747699 0.029708 -1.310446 2.620265 \n",
"265 -0.579746 2.246286 0.968432 -1.288886 -0.003273 -0.503409 0.023601 \n",
"786 1.367241 2.337107 -1.382961 0.925568 -1.373163 -0.581355 -1.057320 \n",
"563 -0.487488 -0.303342 -2.960748 1.385569 -1.367310 1.270372 0.489614 \n",
"145 -2.808225 1.476268 -0.669289 0.837867 0.318861 0.101554 -0.005306 \n",
"\n",
" X0 X1 \n",
"241 -0.048085 -2.852733 \n",
"896 -1.475853 -0.322351 \n",
"723 0.546637 -0.382725 \n",
"758 1.138511 -1.096648 \n",
"649 1.686361 -0.172366 \n",
".. ... ... \n",
"177 2.620265 -1.310446 \n",
"265 0.023601 -0.503409 \n",
"786 -1.057320 -0.581355 \n",
"563 0.489614 1.270372 \n",
"145 -0.005306 0.101554 \n",
"\n",
"[1000 rows x 9 columns], 'y': 241 7.793042\n",
"896 -7.004058\n",
"723 12.592456\n",
"758 12.802985\n",
"649 11.515556\n",
" ... \n",
"177 13.887378\n",
"265 9.003069\n",
"786 8.954078\n",
"563 -5.142521\n",
"145 -4.715614\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 241 True\n",
"896 True\n",
"723 True\n",
"758 True\n",
"649 True\n",
" ... \n",
"177 True\n",
"265 True\n",
"786 True\n",
"563 False\n",
"145 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9464\n",
"INFO:causalml: RMSE (Treatment): 0.9755\n",
"INFO:causalml: sMAPE (Control): 0.4851\n",
"INFO:causalml: sMAPE (Treatment): 0.1754\n",
"INFO:causalml: Gini (Control): 0.7484\n",
"INFO:causalml: Gini (Treatment): 0.9879\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"303 -1.782573 1.161716 -0.132032 1.247322 0.906861 -0.589598 0.566135 \n",
"928 -0.082647 -0.854352 -0.877404 0.258306 -0.690152 0.621097 -1.202361 \n",
"188 1.870551 1.402824 -0.641787 0.898412 -0.955153 0.086009 0.396813 \n",
"7 0.134461 1.635799 -1.651504 0.903543 -1.832069 -0.771336 1.027382 \n",
"536 -1.413058 0.331189 -0.549325 -0.161507 -0.050805 1.085822 -1.137920 \n",
".. ... ... ... ... ... ... ... \n",
"498 1.018865 1.167549 0.300948 0.499916 0.024608 -0.209970 0.662375 \n",
"42 0.685643 0.464868 -0.530367 1.008595 1.087561 0.792421 0.560773 \n",
"540 -1.459292 1.994080 -0.337395 0.007646 2.188399 0.628455 1.352454 \n",
"83 -0.635356 -0.756915 0.860081 0.351036 -0.501081 -0.642911 -0.373147 \n",
"781 -0.997105 1.206782 -0.466358 -0.047794 -0.294843 -1.540877 0.881991 \n",
"\n",
" X0 X1 \n",
"303 0.566135 -0.589598 \n",
"928 -1.202361 0.621097 \n",
"188 0.396813 0.086009 \n",
"7 1.027382 -0.771336 \n",
"536 -1.137920 1.085822 \n",
".. ... ... \n",
"498 0.662375 -0.209970 \n",
"42 0.560773 0.792421 \n",
"540 1.352454 0.628455 \n",
"83 -0.373147 -0.642911 \n",
"781 0.881991 -1.540877 \n",
"\n",
"[1000 rows x 9 columns], 'y': 303 11.687300\n",
"928 3.466874\n",
"188 17.138200\n",
"7 -2.646268\n",
"536 3.123954\n",
" ... \n",
"498 17.581110\n",
"42 19.574715\n",
"540 19.517995\n",
"83 5.781616\n",
"781 8.454186\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 303 True\n",
"928 True\n",
"188 True\n",
"7 False\n",
"536 True\n",
" ... \n",
"498 True\n",
"42 True\n",
"540 True\n",
"83 True\n",
"781 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9429\n",
"INFO:causalml: RMSE (Treatment): 0.9998\n",
"INFO:causalml: sMAPE (Control): 0.5267\n",
"INFO:causalml: sMAPE (Treatment): 0.1899\n",
"INFO:causalml: Gini (Control): 0.7351\n",
"INFO:causalml: Gini (Treatment): 0.9890\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"955 -0.497739 0.540374 -1.677981 1.349484 2.091278 -1.039961 1.206380 \n",
"435 -1.256109 1.764761 -0.149741 0.355327 -0.774481 0.614483 1.777844 \n",
"354 -2.424064 2.357013 -0.394928 -0.458982 1.377004 0.445340 0.305403 \n",
"251 -1.935402 -0.583126 0.125224 1.325398 -0.135370 -0.313797 0.341698 \n",
"479 -2.307127 0.566961 -0.554632 -0.132100 -2.127523 0.432313 -0.415788 \n",
".. ... ... ... ... ... ... ... \n",
"32 -0.982458 0.620985 0.106409 -0.629536 -1.279116 0.532026 0.410600 \n",
"233 0.083363 1.012340 -0.976858 0.790091 0.179734 -1.685756 3.030718 \n",
"26 -0.078329 0.136605 0.222189 2.949437 -0.460346 -1.743903 1.989701 \n",
"379 -2.647414 2.197177 -0.438679 -0.673437 0.049419 -1.671370 0.620090 \n",
"845 -1.097083 -0.445490 -1.364601 0.971446 -1.841247 0.187582 -0.453287 \n",
"\n",
" X0 X1 \n",
"955 1.206380 -1.039961 \n",
"435 1.777844 0.614483 \n",
"354 0.305403 0.445340 \n",
"251 0.341698 -0.313797 \n",
"479 -0.415788 0.432313 \n",
".. ... ... \n",
"32 0.410600 0.532026 \n",
"233 3.030718 -1.685756 \n",
"26 1.989701 -1.743903 \n",
"379 0.620090 -1.671370 \n",
"845 -0.453287 0.187582 \n",
"\n",
"[1000 rows x 9 columns], 'y': 955 18.503792\n",
"435 13.857254\n",
"354 9.572774\n",
"251 5.436045\n",
"479 -4.199785\n",
" ... \n",
"32 -5.941016\n",
"233 21.285516\n",
"26 17.468518\n",
"379 -6.731177\n",
"845 -8.309502\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 955 True\n",
"435 True\n",
"354 True\n",
"251 True\n",
"479 True\n",
" ... \n",
"32 False\n",
"233 True\n",
"26 True\n",
"379 False\n",
"845 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9546\n",
"INFO:causalml: RMSE (Treatment): 0.8873\n",
"INFO:causalml: sMAPE (Control): 0.5285\n",
"INFO:causalml: sMAPE (Treatment): 0.1608\n",
"INFO:causalml: Gini (Control): 0.7781\n",
"INFO:causalml: Gini (Treatment): 0.9905\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"137 -1.870239 1.002550 -1.280447 0.096607 -1.693014 -0.218161 -0.988356 \n",
"265 -0.513343 2.114772 0.996179 -1.443963 -0.114143 -0.406941 -0.045786 \n",
"777 -0.493441 1.716078 0.792791 -0.043363 -0.556987 -1.268526 1.451878 \n",
"5 0.081454 -0.180901 -0.354700 -1.459105 0.296656 -0.759733 -0.701632 \n",
"487 -1.420850 0.398972 1.112433 -0.228356 0.349759 -1.154691 -0.299466 \n",
".. ... ... ... ... ... ... ... \n",
"467 -1.547984 1.672450 0.493790 -0.006961 1.256418 0.077298 0.376628 \n",
"118 -1.453238 0.945857 0.236851 -0.637282 -0.686940 0.717364 0.140900 \n",
"866 -2.650644 -0.029222 -0.964702 -0.443857 0.949448 0.071478 0.590612 \n",
"776 -2.793850 -0.766315 -0.437998 0.125062 -1.087616 0.492760 1.852926 \n",
"454 -0.520798 0.402434 -0.807018 0.681877 -1.949777 0.007282 2.327974 \n",
"\n",
" X0 X1 \n",
"137 -0.988356 -0.218161 \n",
"265 -0.045786 -0.406941 \n",
"777 1.451878 -1.268526 \n",
"5 -0.701632 -0.759733 \n",
"487 -0.299466 -1.154691 \n",
".. ... ... \n",
"467 0.376628 0.077298 \n",
"118 0.140900 0.717364 \n",
"866 0.590612 0.071478 \n",
"776 1.852926 0.492760 \n",
"454 2.327974 0.007282 \n",
"\n",
"[1000 rows x 9 columns], 'y': 137 -9.707122\n",
"265 9.003069\n",
"777 12.450890\n",
"5 4.741680\n",
"487 5.416248\n",
" ... \n",
"467 12.156180\n",
"118 6.682644\n",
"866 6.414822\n",
"776 -12.340879\n",
"454 12.121477\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 137 False\n",
"265 True\n",
"777 True\n",
"5 True\n",
"487 True\n",
" ... \n",
"467 True\n",
"118 True\n",
"866 True\n",
"776 False\n",
"454 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0762\n",
"INFO:causalml: RMSE (Treatment): 0.9945\n",
"INFO:causalml: sMAPE (Control): 0.5339\n",
"INFO:causalml: sMAPE (Treatment): 0.1711\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"829 -1.405635 -0.270187 -1.959880 -0.889368 -1.212302 -1.298171 -0.443938 \n",
"201 -2.536105 0.014067 -1.257571 0.682911 -1.447216 -1.100839 1.130308 \n",
"561 -0.529711 -0.174353 -0.254554 0.894866 -2.286815 0.253255 1.165439 \n",
"370 -1.243201 0.578033 -1.039329 0.953519 -1.614025 -0.207823 0.878261 \n",
"921 -0.681534 0.706844 -0.752564 0.023439 0.770400 -0.849537 1.956007 \n",
".. ... ... ... ... ... ... ... \n",
"47 0.236697 1.161300 -1.267837 -1.410815 -0.159012 -1.005670 1.316541 \n",
"526 1.648612 0.824729 -0.062684 0.566424 0.909295 -0.692820 0.337959 \n",
"67 -0.705354 0.116565 -0.145444 1.079454 -0.611558 -2.607392 -1.660696 \n",
"637 -1.335351 0.667491 -0.005216 -1.587485 -2.347921 -1.555458 1.082437 \n",
"96 1.179559 0.881080 -0.248690 0.957809 -1.554978 -1.030593 1.149389 \n",
"\n",
" X0 X1 \n",
"829 -0.443938 -1.298171 \n",
"201 1.130308 -1.100839 \n",
"561 1.165439 0.253255 \n",
"370 0.878261 -0.207823 \n",
"921 1.956007 -0.849537 \n",
".. ... ... \n",
"47 1.316541 -1.005670 \n",
"526 0.337959 -0.692820 \n",
"67 -1.660696 -2.607392 \n",
"637 1.082437 -1.555458 \n",
"96 1.149389 -1.030593 \n",
"\n",
"[1000 rows x 9 columns], 'y': 829 -9.531238\n",
"201 -10.714346\n",
"561 8.271140\n",
"370 6.533270\n",
"921 0.366548\n",
" ... \n",
"47 11.849394\n",
"526 20.291162\n",
"67 -0.972001\n",
"637 -10.850731\n",
"96 14.140134\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 829 False\n",
"201 False\n",
"561 True\n",
"370 True\n",
"921 False\n",
" ... \n",
"47 True\n",
"526 True\n",
"67 True\n",
"637 False\n",
"96 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Control): 0.7658\n",
"INFO:causalml: Gini (Treatment): 0.9872\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0254\n",
"INFO:causalml: RMSE (Treatment): 0.9947\n",
"INFO:causalml: sMAPE (Control): 0.5244\n",
"INFO:causalml: sMAPE (Treatment): 0.1842\n",
"INFO:causalml: Gini (Control): 0.6910\n",
"INFO:causalml: Gini (Treatment): 0.9870\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"461 -1.460257 -2.060113 0.196816 0.509257 -0.190118 0.640799 1.125688 \n",
"27 -0.764321 0.661496 -1.554778 -0.379143 -0.596560 -1.612376 0.853551 \n",
"546 -2.764439 0.366811 0.121471 0.269416 -1.306595 -0.411430 1.901790 \n",
"222 -0.935723 1.269480 0.057024 -2.368038 -0.694916 -0.264277 1.826955 \n",
"936 -0.594141 -0.764907 -0.392504 -0.899154 -1.017892 -2.733821 1.305824 \n",
".. ... ... ... ... ... ... ... \n",
"353 0.015998 0.982657 -0.392354 -0.194599 -1.400468 0.281264 2.355602 \n",
"366 -1.330955 1.319725 1.896027 -0.476799 1.671268 1.552792 0.045233 \n",
"930 0.223896 0.462045 -0.598557 -0.386784 0.146834 1.176468 -0.409971 \n",
"20 -0.912271 1.216225 -0.653480 1.130985 -0.866986 0.547937 1.677859 \n",
"517 -1.362621 -0.010860 -0.360578 1.211214 -0.976836 -1.004424 1.073958 \n",
"\n",
" X0 X1 \n",
"461 1.125688 0.640799 \n",
"27 0.853551 -1.612376 \n",
"546 1.901790 -0.411430 \n",
"222 1.826955 -0.264277 \n",
"936 1.305824 -2.733821 \n",
".. ... ... \n",
"353 2.355602 0.281264 \n",
"366 0.045233 1.552792 \n",
"930 -0.409971 1.176468 \n",
"20 1.677859 0.547937 \n",
"517 1.073958 -1.004424 \n",
"\n",
"[1000 rows x 9 columns], 'y': 461 -6.434741\n",
"27 -3.423349\n",
"546 -11.053337\n",
"222 9.575547\n",
"936 4.998172\n",
" ... \n",
"353 16.031327\n",
"366 13.879918\n",
"930 10.114187\n",
"20 14.509975\n",
"517 -4.981074\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 461 False\n",
"27 False\n",
"546 False\n",
"222 True\n",
"936 True\n",
" ... \n",
"353 True\n",
"366 True\n",
"930 True\n",
"20 True\n",
"517 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1047\n",
"INFO:causalml: RMSE (Treatment): 1.0173\n",
"INFO:causalml: sMAPE (Control): 0.5298\n",
"INFO:causalml: sMAPE (Treatment): 0.1928\n",
"INFO:causalml: Gini (Control): 0.7360\n",
"INFO:causalml: Gini (Treatment): 0.9868\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"879 -1.256963 2.610828 0.183906 -0.127617 -1.530665 0.406917 -0.949262 \n",
"507 -1.625075 -0.163592 -1.552466 0.974451 -1.413166 -2.116304 0.715120 \n",
"5 0.117539 -0.237488 -0.331980 -1.762631 0.041748 -0.904643 -0.577618 \n",
"503 -1.927225 2.080508 -1.491481 0.302042 0.310731 -0.307241 1.247553 \n",
"398 -0.421897 1.548984 -1.098997 0.506289 -1.730921 -1.446963 0.408468 \n",
".. ... ... ... ... ... ... ... \n",
"27 -0.754819 0.708271 -1.279052 -0.231437 -0.561031 -1.538710 1.170755 \n",
"772 -1.641683 0.455094 -1.104356 1.465097 -0.395100 -1.410726 -0.320576 \n",
"509 0.454215 0.351701 -0.295182 0.429296 0.286542 -1.281868 -0.422850 \n",
"643 -0.714246 2.659219 -1.015225 1.523564 -3.134931 1.156423 1.157304 \n",
"998 -0.208078 0.901069 -1.635665 -0.081512 -1.318741 0.756215 1.485413 \n",
"\n",
" X0 X1 \n",
"879 -0.949262 0.406917 \n",
"507 0.715120 -2.116304 \n",
"5 -0.577618 -0.904643 \n",
"503 1.247553 -0.307241 \n",
"398 0.408468 -1.446963 \n",
".. ... ... \n",
"27 1.170755 -1.538710 \n",
"772 -0.320576 -1.410726 \n",
"509 -0.422850 -1.281868 \n",
"643 1.157304 1.156423 \n",
"998 1.485413 0.756215 \n",
"\n",
"[1000 rows x 9 columns], 'y': 879 2.556286\n",
"507 -8.258449\n",
"5 4.741680\n",
"503 -3.163402\n",
"398 -5.105764\n",
" ... \n",
"27 -3.423349\n",
"772 3.250030\n",
"509 10.361696\n",
"643 9.118838\n",
"998 13.321122\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 879 True\n",
"507 False\n",
"5 True\n",
"503 False\n",
"398 False\n",
" ... \n",
"27 False\n",
"772 True\n",
"509 True\n",
"643 True\n",
"998 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0923\n",
"INFO:causalml: RMSE (Treatment): 0.8748\n",
"INFO:causalml: sMAPE (Control): 0.5358\n",
"INFO:causalml: sMAPE (Treatment): 0.1645\n",
"INFO:causalml: Gini (Control): 0.6935\n",
"INFO:causalml: Gini (Treatment): 0.9895\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"182 -0.858135 0.548027 0.570832 1.543022 -2.357673 1.666628 2.422103 \n",
"581 -0.420050 2.640835 -1.237284 -0.816649 -1.431552 -1.135704 1.275244 \n",
"381 0.220628 0.129095 -0.082455 -0.835143 -1.065214 -0.080231 -1.145132 \n",
"877 -0.263394 0.773930 -0.167360 -1.809891 1.394062 1.191594 0.087068 \n",
"123 -0.820337 1.260770 -0.463931 1.269620 -2.509042 1.125975 -1.084071 \n",
".. ... ... ... ... ... ... ... \n",
"413 -0.352489 1.333771 -2.830226 -0.970291 -2.868858 -0.818934 0.160747 \n",
"725 -1.069626 1.185964 -2.415549 0.944403 -0.280930 1.234655 -1.314084 \n",
"300 -1.442654 0.561198 -0.598452 1.170720 -1.425032 -0.104172 2.707997 \n",
"533 -1.916023 2.135901 -0.882891 -0.073193 -2.437310 -1.498964 -0.956094 \n",
"746 0.635679 0.256677 -0.002004 -0.305042 0.331563 0.584977 -0.708696 \n",
"\n",
" X0 X1 \n",
"182 2.422103 1.666628 \n",
"581 1.275244 -1.135704 \n",
"381 -1.145132 -0.080231 \n",
"877 0.087068 1.191594 \n",
"123 -1.084071 1.125975 \n",
".. ... ... \n",
"413 0.160747 -0.818934 \n",
"725 -1.314084 1.234655 \n",
"300 2.707997 -0.104172 \n",
"533 -0.956094 -1.498964 \n",
"746 -0.708696 0.584977 \n",
"\n",
"[1000 rows x 9 columns], 'y': 182 15.277732\n",
"581 10.512753\n",
"381 2.059536\n",
"877 12.698322\n",
"123 1.168663\n",
" ... \n",
"413 -10.466301\n",
"725 -3.118501\n",
"300 13.493938\n",
"533 -6.739186\n",
"746 10.903542\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 182 True\n",
"581 True\n",
"381 True\n",
"877 True\n",
"123 True\n",
" ... \n",
"413 False\n",
"725 False\n",
"300 True\n",
"533 True\n",
"746 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0416\n",
"INFO:causalml: RMSE (Treatment): 1.0407\n",
"INFO:causalml: sMAPE (Control): 0.5279\n",
"INFO:causalml: sMAPE (Treatment): 0.1668\n",
"INFO:causalml: Gini (Control): 0.6798\n",
"INFO:causalml: Gini (Treatment): 0.9840\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9505\n",
"INFO:causalml: RMSE (Treatment): 0.9570\n",
"INFO:causalml: sMAPE (Control): 0.5073\n",
"INFO:causalml: sMAPE (Treatment): 0.1837\n",
"INFO:causalml: Gini (Control): 0.7110\n",
"INFO:causalml: Gini (Treatment): 0.9879\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"521 -1.064810 2.125579 -2.096933 0.238321 -1.922263 -0.778079 -0.661939 \n",
"277 -0.958657 0.357395 0.086754 -0.898277 -0.734021 -1.876077 -0.094653 \n",
"161 -2.661257 1.594218 -1.232403 -1.302777 -1.473713 1.516190 2.922502 \n",
"708 -0.420950 1.218293 0.079718 -0.321749 -0.314454 -1.630925 1.536317 \n",
"810 -0.607251 0.596658 0.352069 0.448846 -0.316254 -0.086533 -0.948286 \n",
".. ... ... ... ... ... ... ... \n",
"549 1.102751 0.590279 -1.566217 -0.906844 0.194944 -0.075375 0.976922 \n",
"735 -0.530474 0.449591 -2.149441 -0.581266 0.593925 -1.157048 2.266023 \n",
"850 -1.613409 1.467119 0.187807 1.032850 -2.193383 -0.478503 2.370121 \n",
"800 -2.437974 0.813644 0.292719 1.129961 0.010590 0.636060 -1.370904 \n",
"282 -0.160975 -0.195619 0.574784 1.624199 -1.756967 -1.462474 0.626548 \n",
"\n",
" X0 X1 \n",
"521 -0.661939 -0.778079 \n",
"277 -0.094653 -1.876077 \n",
"161 2.922502 1.516190 \n",
"708 1.536317 -1.630925 \n",
"810 -0.948286 -0.086533 \n",
".. ... ... \n",
"549 0.976922 -0.075375 \n",
"735 2.266023 -1.157048 \n",
"850 2.370121 -0.478503 \n",
"800 -1.370904 0.636060 \n",
"282 0.626548 -1.462474 \n",
"\n",
"[1000 rows x 9 columns], 'y': 521 0.541061\n",
"277 1.863805\n",
"161 -11.664097\n",
"708 12.369009\n",
"810 5.956647\n",
" ... \n",
"549 15.679880\n",
"735 15.212283\n",
"850 12.795708\n",
"800 2.492589\n",
"282 7.672536\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 521 True\n",
"277 True\n",
"161 False\n",
"708 True\n",
"810 True\n",
" ... \n",
"549 True\n",
"735 True\n",
"850 True\n",
"800 True\n",
"282 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"335 -1.100336 1.977221 0.607966 -0.793885 -2.545464 1.366718 0.809280 \n",
"171 -0.991677 1.353156 -1.437487 -0.719049 -1.376758 0.623510 -0.261505 \n",
"148 -0.250226 -0.427685 -1.350273 -0.859032 -1.316345 -1.337075 1.060074 \n",
"337 -2.354606 0.835613 -1.931366 -0.876002 -0.937969 -0.123990 -0.279434 \n",
"550 -0.407100 -0.483698 0.309698 -0.047278 0.531595 -1.814945 0.574206 \n",
".. ... ... ... ... ... ... ... \n",
"899 -0.628691 -0.756683 -0.238593 -1.000216 -1.325123 -0.779234 -0.059174 \n",
"796 -0.102175 1.460876 -0.167504 -1.664169 -0.077747 0.248961 -0.648353 \n",
"898 -1.258328 1.939644 -0.848240 1.668798 0.619521 -1.431741 2.092603 \n",
"399 -1.386327 2.762234 0.245965 0.029650 1.174612 -2.486153 0.375883 \n",
"982 -0.803224 0.691163 -0.389384 -0.943258 0.847790 -1.253997 1.159258 \n",
"\n",
" X0 X1 \n",
"335 0.809280 1.366718 \n",
"171 -0.261505 0.623510 \n",
"148 1.060074 -1.337075 \n",
"337 -0.279434 -0.123990 \n",
"550 0.574206 -1.814945 \n",
".. ... ... \n",
"899 -0.059174 -0.779234 \n",
"796 -0.648353 0.248961 \n",
"898 2.092603 -1.431741 \n",
"399 0.375883 -2.486153 \n",
"982 1.159258 -1.253997 \n",
"\n",
"[1000 rows x 9 columns], 'y': 335 6.324474\n",
"171 2.573635\n",
"148 4.946877\n",
"337 -10.149884\n",
"550 9.910321\n",
" ... \n",
"899 2.709571\n",
"796 -1.447993\n",
"898 17.331546\n",
"399 10.567009\n",
"982 12.003112\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 335 True\n",
"171 True\n",
"148 True\n",
"337 False\n",
"550 True\n",
" ... \n",
"899 True\n",
"796 False\n",
"898 True\n",
"399 True\n",
"982 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0987\n",
"INFO:causalml: RMSE (Treatment): 1.0058\n",
"INFO:causalml: sMAPE (Control): 0.5550\n",
"INFO:causalml: sMAPE (Treatment): 0.1759\n",
"INFO:causalml: Gini (Control): 0.7364\n",
"INFO:causalml: Gini (Treatment): 0.9863\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"256 -0.665575 1.778065 -0.968217 -0.127747 0.334922 -0.370859 2.847488 \n",
"417 -0.457640 0.285454 -1.058043 -0.216185 -0.169154 -0.836608 1.797101 \n",
"919 0.785385 1.126725 -0.775216 -0.702748 0.504256 -2.635844 0.545678 \n",
"996 -3.019533 1.626102 -0.388613 1.772885 1.258386 0.954511 1.826523 \n",
"821 -1.469025 1.208626 -3.567248 -0.517454 -1.519949 0.844796 -0.477846 \n",
".. ... ... ... ... ... ... ... \n",
"667 -1.941159 0.823270 -0.024040 1.252969 -0.146037 0.363587 1.403426 \n",
"176 -0.863464 1.495569 0.864471 0.072182 -2.735479 -0.175786 -0.386896 \n",
"761 -1.710151 -0.234718 -0.890572 -1.456842 1.121008 -0.747674 0.901646 \n",
"114 -0.344432 0.510373 -1.191251 0.334169 -1.996054 -1.308779 -0.613666 \n",
"132 -4.244958 1.454520 -0.853378 1.165462 -2.200121 0.261365 0.329199 \n",
"\n",
" X0 X1 \n",
"256 2.847488 -0.370859 \n",
"417 1.797101 -0.836608 \n",
"919 0.545678 -2.635844 \n",
"996 1.826523 0.954511 \n",
"821 -0.477846 0.844796 \n",
".. ... ... \n",
"667 1.403426 0.363587 \n",
"176 -0.386896 -0.175786 \n",
"761 0.901646 -0.747674 \n",
"114 -0.613666 -1.308779 \n",
"132 0.329199 0.261365 \n",
"\n",
"[1000 rows x 9 columns], 'y': 256 20.538593\n",
"417 12.283023\n",
"919 12.794015\n",
"996 17.211685\n",
"821 -9.602174\n",
" ... \n",
"667 11.985876\n",
"176 0.810587\n",
"761 7.959767\n",
"114 0.119504\n",
"132 -3.629107\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 256 True\n",
"417 True\n",
"919 True\n",
"996 True\n",
"821 False\n",
" ... \n",
"667 True\n",
"176 True\n",
"761 True\n",
"114 True\n",
"132 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9008\n",
"INFO:causalml: RMSE (Treatment): 0.9300\n",
"INFO:causalml: sMAPE (Control): 0.5453\n",
"INFO:causalml: sMAPE (Treatment): 0.1736\n",
"INFO:causalml: Gini (Control): 0.7715\n",
"INFO:causalml: Gini (Treatment): 0.9875\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"435 -1.290773 1.787665 -0.154730 0.309899 -0.615941 1.055156 1.949821 \n",
"571 0.073259 -0.265020 1.336202 0.316344 0.232892 -0.748365 1.455606 \n",
"925 -0.241368 -0.555981 -1.886168 1.751812 -1.451546 -1.286078 0.058359 \n",
"191 -0.167786 -0.382653 -2.050806 0.247710 -1.377576 0.097655 -0.163738 \n",
"857 -0.880414 0.756512 -0.296519 -1.008379 -0.378510 -0.025587 -0.555009 \n",
".. ... ... ... ... ... ... ... \n",
"304 -0.657374 0.819624 1.589740 2.162585 0.989363 0.796753 1.805996 \n",
"732 -1.854802 1.650649 -0.613165 0.707549 -0.035249 -0.823780 2.115949 \n",
"469 -1.857866 0.189421 0.310381 -1.897358 -1.033174 -1.519147 1.891197 \n",
"139 -1.429328 0.994971 0.445033 0.053998 0.689442 -1.102233 0.834170 \n",
"405 -3.090734 -0.161113 -0.615230 -0.393858 0.470887 -1.691078 -0.777247 \n",
"\n",
" X0 X1 \n",
"435 1.949821 1.055156 \n",
"571 1.455606 -0.748365 \n",
"925 0.058359 -1.286078 \n",
"191 -0.163738 0.097655 \n",
"857 -0.555009 -0.025587 \n",
".. ... ... \n",
"304 1.805996 0.796753 \n",
"732 2.115949 -0.823780 \n",
"469 1.891197 -1.519147 \n",
"139 0.834170 -1.102233 \n",
"405 -0.777247 -1.691078 \n",
"\n",
"[1000 rows x 9 columns], 'y': 435 13.857254\n",
"571 16.753939\n",
"925 4.053751\n",
"191 -5.492128\n",
"857 4.361211\n",
" ... \n",
"304 22.259686\n",
"732 -3.133831\n",
"469 4.435916\n",
"139 11.778033\n",
"405 -8.503448\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 435 True\n",
"571 True\n",
"925 True\n",
"191 False\n",
"857 True\n",
" ... \n",
"304 True\n",
"732 False\n",
"469 True\n",
"139 True\n",
"405 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"373 -0.586068 0.785214 -0.258682 2.335104 -0.514019 0.671037 -0.047911 \n",
"740 -1.730376 -0.082488 -0.860927 1.462483 0.614116 -0.480573 2.059247 \n",
"501 -1.551334 1.009595 1.256427 -0.197566 -2.084231 -0.446778 0.288796 \n",
"220 0.878591 1.244577 -0.156619 0.477556 -1.101665 -1.706181 0.564847 \n",
"176 -0.686087 1.487416 0.889637 0.124132 -2.654856 -0.027463 -0.529537 \n",
".. ... ... ... ... ... ... ... \n",
"975 -1.038536 0.544160 0.204063 2.023977 -0.641787 -1.154168 0.788529 \n",
"783 -1.590418 1.712067 -1.071416 -2.106101 -0.628625 0.294950 0.044412 \n",
"845 -1.343336 -0.556841 -1.157557 0.820337 -1.689095 0.090289 -0.518620 \n",
"731 -0.748152 2.179917 -1.198134 0.162754 -0.255301 -0.187085 1.350099 \n",
"437 -1.814920 2.655633 0.842024 0.082480 -1.483412 -1.248605 -0.208832 \n",
"\n",
" X0 X1 \n",
"373 -0.047911 0.671037 \n",
"740 2.059247 -0.480573 \n",
"501 0.288796 -0.446778 \n",
"220 0.564847 -1.706181 \n",
"176 -0.529537 -0.027463 \n",
".. ... ... \n",
"975 0.788529 -1.154168 \n",
"783 0.044412 0.294950 \n",
"845 -0.518620 0.090289 \n",
"731 1.350099 -0.187085 \n",
"437 -0.208832 -1.248605 \n",
"\n",
"[1000 rows x 9 columns], 'y': 373 12.268099\n",
"740 15.715393\n",
"501 2.593155\n",
"220 10.878908\n",
"176 0.810587\n",
" ... \n",
"975 9.860888\n",
"783 2.764437\n",
"845 -8.309502\n",
"731 13.912472\n",
"437 1.751543\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 373 True\n",
"740 True\n",
"501 True\n",
"220 True\n",
"176 True\n",
" ... \n",
"975 True\n",
"783 True\n",
"845 False\n",
"731 True\n",
"437 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.2452\n",
"INFO:causalml: RMSE (Treatment): 1.0504\n",
"INFO:causalml: sMAPE (Control): 0.5990\n",
"INFO:causalml: sMAPE (Treatment): 0.1656\n",
"INFO:causalml: Gini (Control): 0.7487\n",
"INFO:causalml: Gini (Treatment): 0.9861\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1500\n",
"INFO:causalml: RMSE (Treatment): 1.0641\n",
"INFO:causalml: sMAPE (Control): 0.5799\n",
"INFO:causalml: sMAPE (Treatment): 0.1953\n",
"INFO:causalml: Gini (Control): 0.7139\n",
"INFO:causalml: Gini (Treatment): 0.9853\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"476 -0.916246 0.716053 1.561100 1.401698 -0.298811 1.040683 1.057571 \n",
"96 1.086606 0.844974 -0.116830 0.902299 -1.404204 -1.070461 1.049181 \n",
"350 -0.488393 1.011535 0.916039 0.266851 -0.692067 -1.712296 0.453096 \n",
"247 -1.185833 0.211358 0.441802 0.973059 -0.087143 -1.599477 -0.505370 \n",
"85 -1.717485 1.374249 -0.641572 -0.273738 -0.978240 -2.893886 -0.306468 \n",
".. ... ... ... ... ... ... ... \n",
"676 -1.635484 2.555525 -0.922551 1.692212 -2.022160 0.377001 0.911202 \n",
"955 -0.502191 0.691452 -1.555526 1.187045 1.790498 -0.879598 1.108326 \n",
"624 -2.064699 -0.396411 0.015957 1.326630 -1.316539 0.201398 1.078927 \n",
"164 -2.005248 0.401852 -1.029118 0.011918 -1.026978 -1.102522 0.124741 \n",
"614 -2.496101 1.064544 -0.721495 0.237056 0.136538 -0.549725 -0.119093 \n",
"\n",
" X0 X1 \n",
"476 1.057571 1.040683 \n",
"96 1.049181 -1.070461 \n",
"350 0.453096 -1.712296 \n",
"247 -0.505370 -1.599477 \n",
"85 -0.306468 -2.893886 \n",
".. ... ... \n",
"676 0.911202 0.377001 \n",
"955 1.108326 -0.879598 \n",
"624 1.078927 0.201398 \n",
"164 0.124741 -1.102522 \n",
"614 -0.119093 -0.549725 \n",
"\n",
"[1000 rows x 9 columns], 'y': 476 13.134949\n",
"96 14.140134\n",
"350 8.901755\n",
"247 3.320266\n",
"85 -0.384362\n",
" ... \n",
"676 8.554497\n",
"955 18.503792\n",
"624 5.810979\n",
"164 1.757613\n",
"614 -5.992837\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 476 True\n",
"96 True\n",
"350 True\n",
"247 True\n",
"85 True\n",
" ... \n",
"676 True\n",
"955 True\n",
"624 True\n",
"164 True\n",
"614 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0893\n",
"INFO:causalml: RMSE (Treatment): 1.0560\n",
"INFO:causalml: sMAPE (Control): 0.5163\n",
"INFO:causalml: sMAPE (Treatment): 0.1890\n",
"INFO:causalml: Gini (Control): 0.6924\n",
"INFO:causalml: Gini (Treatment): 0.9841\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1185\n",
"INFO:causalml: RMSE (Treatment): 1.0528\n",
"INFO:causalml: sMAPE (Control): 0.4944\n",
"INFO:causalml: sMAPE (Treatment): 0.1829\n",
"INFO:causalml: Gini (Control): 0.6812\n",
"INFO:causalml: Gini (Treatment): 0.9845\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0523\n",
"INFO:causalml: RMSE (Treatment): 1.0966\n",
"INFO:causalml: sMAPE (Control): 0.5335\n",
"INFO:causalml: sMAPE (Treatment): 0.1846\n",
"INFO:causalml: Gini (Control): 0.7403\n",
"INFO:causalml: Gini (Treatment): 0.9842\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"656 -1.365084 1.819157 2.098686 0.874874 1.299507 -0.600109 0.545343 \n",
"607 -0.706812 -0.877614 -0.315875 -0.408886 -0.483012 -0.921114 -0.109378 \n",
"342 -1.679826 -0.614213 -1.995008 -0.124394 0.000163 -1.293688 0.397625 \n",
"843 -1.884440 2.477489 1.248540 1.445514 -0.693600 -1.420867 -0.105174 \n",
"574 -0.478880 0.360457 -1.256787 1.173270 -0.719220 -1.092105 -0.236187 \n",
".. ... ... ... ... ... ... ... \n",
"348 -0.907488 1.956598 -1.850559 0.326344 -1.404297 -1.670213 1.590651 \n",
"181 1.288764 0.127013 -0.064689 0.743168 -0.541843 -0.899438 0.210216 \n",
"126 -0.216085 2.137236 0.513100 -0.728803 -0.439462 -1.680363 0.199879 \n",
"804 -2.459387 0.782972 -0.055967 1.354856 -1.178010 -1.081715 0.546584 \n",
"741 -0.609507 2.797551 -1.293786 -0.905862 -1.302123 -0.453713 0.131056 \n",
"\n",
" X0 X1 \n",
"656 0.545343 -0.600109 \n",
"607 -0.109378 -0.921114 \n",
"342 0.397625 -1.293688 \n",
"843 -0.105174 -1.420867 \n",
"574 -0.236187 -1.092105 \n",
".. ... ... \n",
"348 1.590651 -1.670213 \n",
"181 0.210216 -0.899438 \n",
"126 0.199879 -1.680363 \n",
"804 0.546584 -1.081715 \n",
"741 0.131056 -0.453713 \n",
"\n",
"[1000 rows x 9 columns], 'y': 656 15.632059\n",
"607 3.571733\n",
"342 -6.856146\n",
"843 6.032900\n",
"574 5.633909\n",
" ... \n",
"348 9.469476\n",
"181 13.569762\n",
"126 9.175909\n",
"804 2.906483\n",
"741 5.593102\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 656 True\n",
"607 True\n",
"342 False\n",
"843 True\n",
"574 True\n",
" ... \n",
"348 True\n",
"181 True\n",
"126 True\n",
"804 True\n",
"741 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"766 -0.756667 0.367923 1.153857 1.799840 0.228541 -0.160941 0.239082 \n",
"295 -0.920244 0.816180 -1.724668 0.524165 -1.332265 0.253598 1.161850 \n",
"413 -0.358793 1.190467 -2.753001 -1.067620 -3.125181 -0.715718 -0.055546 \n",
"293 -1.984653 -1.143055 -1.910437 -1.334067 -0.775120 -1.530153 0.337544 \n",
"669 -0.053879 0.915218 -1.183049 0.444186 0.486040 -2.352234 -0.881775 \n",
".. ... ... ... ... ... ... ... \n",
"475 -1.420390 -1.741700 -1.396914 -1.520924 -0.835104 -0.095234 2.793876 \n",
"136 -1.755542 -0.374752 -1.002181 1.103858 0.006548 0.715375 -0.163707 \n",
"421 -2.105743 0.172084 -0.756788 -0.443594 -0.113048 0.950566 -2.126405 \n",
"857 -0.822371 0.714026 -0.458390 -0.895779 -0.553540 0.003880 -0.725634 \n",
"977 -1.684132 0.534968 -1.003727 0.342221 0.432128 -0.191376 1.501638 \n",
"\n",
" X0 X1 \n",
"766 0.239082 -0.160941 \n",
"295 1.161850 0.253598 \n",
"413 -0.055546 -0.715718 \n",
"293 0.337544 -1.530153 \n",
"669 -0.881775 -2.352234 \n",
".. ... ... \n",
"475 2.793876 -0.095234 \n",
"136 -0.163707 0.715375 \n",
"421 -2.126405 0.950566 \n",
"857 -0.725634 0.003880 \n",
"977 1.501638 -0.191376 \n",
"\n",
"[1000 rows x 9 columns], 'y': 766 12.119461\n",
"295 9.933260\n",
"413 -10.466301\n",
"293 -11.609205\n",
"669 6.961904\n",
" ... \n",
"475 -10.586297\n",
"136 -4.977571\n",
"421 -2.608507\n",
"857 4.361211\n",
"977 -3.568162\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 766 True\n",
"295 True\n",
"413 False\n",
"293 False\n",
"669 True\n",
" ... \n",
"475 False\n",
"136 False\n",
"421 True\n",
"857 True\n",
"977 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"716 -1.780908 1.296653 -1.506171 -1.069888 1.365460 -0.138733 0.313991 \n",
"932 -1.299847 0.588967 -0.107455 0.283732 -1.323747 0.130964 -0.733880 \n",
"582 -1.949519 1.287445 -0.375304 1.762713 -0.174648 -0.675732 0.814971 \n",
"28 0.228184 -1.669546 -0.676058 -1.615774 -0.315091 -0.476678 -0.171973 \n",
"761 -1.902200 -0.095299 -0.949139 -1.353268 0.986338 -0.829036 0.806286 \n",
".. ... ... ... ... ... ... ... \n",
"207 -1.325801 1.102182 1.192057 0.460207 1.410201 -2.357977 0.753458 \n",
"914 -0.005436 1.224618 0.435307 -0.579745 -0.861379 0.677591 0.691308 \n",
"321 -2.029734 -0.232718 0.728393 0.233231 -0.613002 -0.260258 0.602071 \n",
"774 -0.740094 0.123906 0.391809 -0.824587 0.195687 -1.391723 0.153809 \n",
"41 0.107240 -0.096356 -0.831740 2.298990 -0.212071 -0.017084 0.168104 \n",
"\n",
" X0 X1 \n",
"716 0.313991 -0.138733 \n",
"932 -0.733880 0.130964 \n",
"582 0.814971 -0.675732 \n",
"28 -0.171973 -0.476678 \n",
"761 0.806286 -0.829036 \n",
".. ... ... \n",
"207 0.753458 -2.357977 \n",
"914 0.691308 0.677591 \n",
"321 0.602071 -0.260258 \n",
"774 0.153809 -1.391723 \n",
"41 0.168104 -0.017084 \n",
"\n",
"[1000 rows x 9 columns], 'y': 716 9.358799\n",
"932 1.654133\n",
"582 10.549091\n",
"28 4.753158\n",
"761 7.959767\n",
" ... \n",
"207 13.068250\n",
"914 12.801840\n",
"321 -6.946700\n",
"774 6.080336\n",
"41 12.011428\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 716 True\n",
"932 True\n",
"582 True\n",
"28 True\n",
"761 True\n",
" ... \n",
"207 True\n",
"914 True\n",
"321 False\n",
"774 True\n",
"41 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.2193\n",
"INFO:causalml: RMSE (Treatment): 0.9058\n",
"INFO:causalml: sMAPE (Control): 0.5661\n",
"INFO:causalml: sMAPE (Treatment): 0.1605\n",
"INFO:causalml: Gini (Control): 0.6898\n",
"INFO:causalml: Gini (Treatment): 0.9892\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0219\n",
"INFO:causalml: RMSE (Treatment): 0.8973\n",
"INFO:causalml: sMAPE (Control): 0.5490\n",
"INFO:causalml: sMAPE (Treatment): 0.1593\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"535 -0.408946 -0.549911 -0.438350 0.654294 -0.431326 -1.569354 2.919030 \n",
"813 -2.613072 1.682590 -1.177321 0.400974 0.448995 -0.975483 0.266881 \n",
"702 1.737639 1.568111 -0.021695 -0.401659 1.804794 0.047483 0.487667 \n",
"520 -1.447681 0.708456 -0.474129 -0.049508 -0.575110 -0.862941 1.229650 \n",
"489 -0.389307 -1.466011 -0.718566 0.734077 0.085211 -1.077799 -1.288199 \n",
".. ... ... ... ... ... ... ... \n",
"961 -1.446214 1.654762 -0.271929 1.143991 -0.885921 -1.901416 0.019326 \n",
"57 0.172187 0.239910 -0.921379 0.628416 0.606811 -0.931338 2.313877 \n",
"541 0.332641 -0.475919 -0.267263 -1.407782 0.013615 -0.561815 -0.244886 \n",
"659 -0.818998 2.868143 -0.850733 2.637302 -1.828988 -0.761172 -0.675929 \n",
"877 -0.220138 0.794346 -0.213728 -1.750195 1.452935 1.435132 -0.171223 \n",
"\n",
" X0 X1 \n",
"535 2.919030 -1.569354 \n",
"813 0.266881 -0.975483 \n",
"702 0.487667 0.047483 \n",
"520 1.229650 -0.862941 \n",
"489 -1.288199 -1.077799 \n",
".. ... ... \n",
"961 0.019326 -1.901416 \n",
"57 2.313877 -0.931338 \n",
"541 -0.244886 -0.561815 \n",
"659 -0.675929 -0.761172 \n",
"877 -0.171223 1.435132 \n",
"\n",
"[1000 rows x 9 columns], 'y': 535 16.319324\n",
"813 5.390940\n",
"702 23.877017\n",
"520 -5.145441\n",
"489 2.992182\n",
" ... \n",
"961 4.732956\n",
"57 18.788397\n",
"541 5.970889\n",
"659 4.772285\n",
"877 12.698322\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 535 True\n",
"813 True\n",
"702 True\n",
"520 False\n",
"489 True\n",
" ... \n",
"961 True\n",
"57 True\n",
"541 True\n",
"659 True\n",
"877 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"809 -1.752726 1.478834 0.359537 1.257853 -1.355993 -0.610185 0.942202 \n",
"474 -0.345282 2.676627 -0.363979 0.739986 1.029841 -0.808572 0.177291 \n",
"51 -0.311615 0.463675 1.315166 -0.177182 -0.411103 -0.774316 -0.120261 \n",
"706 -0.692079 2.126244 0.262442 0.057324 0.769064 -0.869747 2.342587 \n",
"42 0.652502 0.343937 -0.589744 1.123646 0.894766 0.882823 0.656313 \n",
".. ... ... ... ... ... ... ... \n",
"519 -1.076340 1.531144 -0.136154 -0.171633 -1.209391 -0.787394 0.436260 \n",
"982 -0.838780 0.578530 -0.508228 -0.862035 0.874482 -1.286068 1.319914 \n",
"736 -0.812325 1.159239 -1.139383 1.176134 -1.104442 -1.210979 0.111341 \n",
"806 -0.847931 -0.003755 0.040395 0.548220 -0.999150 -2.048184 1.979963 \n",
"811 -1.703399 0.515531 -1.691708 1.993570 -0.182223 -2.128436 1.939605 \n",
"\n",
" X0 X1 \n",
"809 0.942202 -0.610185 \n",
"474 0.177291 -0.808572 \n",
"51 -0.120261 -0.774316 \n",
"706 2.342587 -0.869747 \n",
"42 0.656313 0.882823 \n",
".. ... ... \n",
"519 0.436260 -0.787394 \n",
"982 1.319914 -1.286068 \n",
"736 0.111341 -1.210979 \n",
"806 1.979963 -2.048184 \n",
"811 1.939605 -2.128436 \n",
"\n",
"[1000 rows x 9 columns], 'y': 809 7.084273\n",
"474 14.285230\n",
"51 8.374283\n",
"706 19.485830\n",
"42 19.574715\n",
" ... \n",
"519 6.513308\n",
"982 12.003112\n",
"736 5.459094\n",
"806 11.154196\n",
"811 10.541175\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 809 True\n",
"474 True\n",
"51 True\n",
"706 True\n",
"42 True\n",
" ... \n",
"519 True\n",
"982 True\n",
"736 True\n",
"806 True\n",
"811 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Control): 0.7354\n",
"INFO:causalml: Gini (Treatment): 0.9894\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9566\n",
"INFO:causalml: RMSE (Treatment): 0.9046\n",
"INFO:causalml: sMAPE (Control): 0.5404\n",
"INFO:causalml: sMAPE (Treatment): 0.1517\n",
"INFO:causalml: Gini (Control): 0.7800\n",
"INFO:causalml: Gini (Treatment): 0.9892\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"710 -0.885544 3.109400 -1.161211 -0.183391 -0.929634 -0.717760 -0.422444 \n",
"288 -1.778829 1.792842 0.514340 0.704523 -1.436816 0.250296 0.553171 \n",
"21 -0.748560 0.992715 -0.587886 1.191375 0.068951 -1.902454 -0.219831 \n",
"852 -3.079856 0.894105 -1.545568 0.883254 0.925119 -1.911790 0.642795 \n",
"605 -0.743595 1.049269 -1.274067 1.230038 -0.519646 -1.730869 0.754537 \n",
".. ... ... ... ... ... ... ... \n",
"61 -0.611891 0.230504 -0.106735 0.888671 -1.792676 -2.140587 -1.199169 \n",
"236 -0.823569 1.054378 0.015185 -1.300129 -1.944497 0.868855 0.267289 \n",
"385 -0.047892 -0.133264 -0.328396 0.389706 -1.938165 -1.366609 -1.458593 \n",
"102 -0.272519 -0.441834 -1.705100 1.615818 0.227272 -0.903417 1.048783 \n",
"818 -2.314737 0.952842 -0.240204 0.218917 -2.342249 -0.645204 1.335878 \n",
"\n",
" X0 X1 \n",
"710 -0.422444 -0.717760 \n",
"288 0.553171 0.250296 \n",
"21 -0.219831 -1.902454 \n",
"852 0.642795 -1.911790 \n",
"605 0.754537 -1.730869 \n",
".. ... ... \n",
"61 -1.199169 -2.140587 \n",
"236 0.267289 0.868855 \n",
"385 -1.458593 -1.366609 \n",
"102 1.048783 -0.903417 \n",
"818 1.335878 -0.645204 \n",
"\n",
"[1000 rows x 9 columns], 'y': 710 5.904376\n",
"288 5.235864\n",
"21 6.406231\n",
"852 4.981610\n",
"605 -1.888067\n",
" ... \n",
"61 -3.292629\n",
"236 4.765758\n",
"385 -1.265529\n",
"102 12.024923\n",
"818 -11.770683\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 710 True\n",
"288 True\n",
"21 True\n",
"852 True\n",
"605 False\n",
" ... \n",
"61 True\n",
"236 True\n",
"385 True\n",
"102 True\n",
"818 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0569\n",
"INFO:causalml: RMSE (Treatment): 0.9878\n",
"INFO:causalml: sMAPE (Control): 0.5790\n",
"INFO:causalml: sMAPE (Treatment): 0.1811\n",
"INFO:causalml: Gini (Control): 0.7231\n",
"INFO:causalml: Gini (Treatment): 0.9886\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0845\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"358 -1.618982 0.336568 -0.776039 1.448581 -1.102834 -0.364277 0.374769 \n",
"540 -1.561058 2.046827 -0.447731 -0.203809 2.188471 0.651892 1.592926 \n",
"74 -1.264685 -0.089928 -2.538475 -2.032437 1.258205 -2.129692 1.397599 \n",
"189 0.623183 0.339064 1.379526 -0.573508 -3.246150 -0.459409 1.117661 \n",
"122 -3.033143 -0.118046 -1.231457 0.914404 0.023980 -1.772121 0.337744 \n",
".. ... ... ... ... ... ... ... \n",
"223 -0.861923 0.334306 0.352111 1.762110 -1.820752 -1.139081 0.854460 \n",
"254 -1.269406 -0.739805 -1.201137 0.641957 0.176942 -0.232125 -1.387830 \n",
"493 -0.951445 2.289435 -1.354908 0.358001 -0.221044 -0.014761 -0.799413 \n",
"883 -1.192269 0.013901 -0.092697 -0.181750 -1.393782 1.118679 0.530328 \n",
"26 0.006493 -0.018154 0.263089 2.895273 -0.498851 -2.131243 2.267322 \n",
"\n",
" X0 X1 \n",
"358 0.374769 -0.364277 \n",
"540 1.592926 0.651892 \n",
"74 1.397599 -2.129692 \n",
"189 1.117661 -0.459409 \n",
"122 0.337744 -1.772121 \n",
".. ... ... \n",
"223 0.854460 -1.139081 \n",
"254 -1.387830 -0.232125 \n",
"493 -0.799413 -0.014761 \n",
"883 0.530328 1.118679 \n",
"26 2.267322 -2.131243 \n",
"\n",
"[1000 rows x 9 columns], 'y': 358 3.577322\n",
"540 19.517995\n",
"74 8.688916\n",
"189 5.702358\n",
"122 -8.281570\n",
" ... \n",
"223 6.636087\n",
"254 0.689958\n",
"493 4.987087\n",
"883 5.901248\n",
"26 17.468518\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 358 True\n",
"540 True\n",
"74 True\n",
"189 True\n",
"122 False\n",
" ... \n",
"223 True\n",
"254 True\n",
"493 True\n",
"883 True\n",
"26 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"240 -1.263348 1.162763 0.105228 1.205755 -0.119909 -1.028827 -0.167676 \n",
"498 0.810539 1.373755 0.219523 0.539263 0.138168 -0.144650 0.582504 \n",
"529 0.630397 -0.221831 -0.178235 -0.483179 -2.770740 -1.892844 0.415426 \n",
"457 -1.354105 1.457178 -0.777315 1.883983 -2.976922 -1.550290 -0.125306 \n",
"2 -1.608387 -0.291487 -0.839419 -0.258104 -1.204207 -0.514901 -0.399287 \n",
".. ... ... ... ... ... ... ... \n",
"929 -1.676347 1.563545 -0.515152 0.216150 -1.656749 0.514116 -0.605672 \n",
"197 -1.603567 0.713467 -2.349028 2.255348 -1.048802 -0.635757 1.187067 \n",
"750 -0.265670 2.101574 -2.127944 -0.954356 0.096180 0.364974 0.481835 \n",
"349 -1.826042 1.028645 -0.118728 0.401023 -0.059445 -0.562672 1.065773 \n",
"477 -3.013714 0.861584 -0.071117 1.361774 -1.356168 -0.940111 1.860255 \n",
"\n",
" X0 X1 \n",
"240 -0.167676 -1.028827 \n",
"498 0.582504 -0.144650 \n",
"529 0.415426 -1.892844 \n",
"457 -0.125306 -1.550290 \n",
"2 -0.399287 -0.514901 \n",
".. ... ... \n",
"929 -0.605672 0.514116 \n",
"197 1.187067 -0.635757 \n",
"750 0.481835 0.364974 \n",
"349 1.065773 -0.562672 \n",
"477 1.860255 -0.940111 \n",
"\n",
"[1000 rows x 9 columns], 'y': 240 6.512254\n",
"498 17.581110\n",
"529 3.288937\n",
"457 -8.028104\n",
"2 -9.777346\n",
" ... \n",
"929 0.739416\n",
"197 -4.810420\n",
"750 -1.151925\n",
"349 10.315484\n",
"477 7.037455\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 240 True\n",
"498 True\n",
"529 True\n",
"457 False\n",
"2 False\n",
" ... \n",
"929 True\n",
"197 False\n",
"750 False\n",
"349 True\n",
"477 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Treatment): 0.9745\n",
"INFO:causalml: sMAPE (Control): 0.5396\n",
"INFO:causalml: sMAPE (Treatment): 0.1887\n",
"INFO:causalml: Gini (Control): 0.7519\n",
"INFO:causalml: Gini (Treatment): 0.9890\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0117\n",
"INFO:causalml: RMSE (Treatment): 0.9768\n",
"INFO:causalml: sMAPE (Control): 0.4914\n",
"INFO:causalml: sMAPE (Treatment): 0.1746\n",
"INFO:causalml: Gini (Control): 0.7261\n",
"INFO:causalml: Gini (Treatment): 0.9861\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8418\n",
"INFO:causalml: RMSE (Treatment): 1.0209\n",
"INFO:causalml: sMAPE (Control): 0.4726\n",
"INFO:causalml: sMAPE (Treatment): 0.1868\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"273 -0.677619 1.186574 -0.958007 1.376116 -1.854810 -0.737435 -2.090940 \n",
"597 -0.418731 0.117396 -1.426846 2.216330 -0.924005 0.298973 -0.106910 \n",
"840 0.815459 0.748352 0.500100 1.252534 -0.255523 -0.079447 0.564455 \n",
"24 -0.617694 0.450154 -1.288916 1.397034 -2.702385 -0.985369 0.468185 \n",
"520 -1.383487 0.444018 -0.638238 0.000878 -0.541441 -0.933725 1.018526 \n",
".. ... ... ... ... ... ... ... \n",
"111 1.145629 0.629617 -0.273566 1.928849 -0.574426 -0.809487 0.827795 \n",
"990 0.189329 0.975162 -0.079997 0.203722 -1.239292 0.415439 -0.101237 \n",
"669 0.261455 1.204097 -0.832557 0.498671 0.600173 -2.208832 -0.891973 \n",
"935 -1.605048 0.441036 0.273670 1.925556 -1.113346 0.059029 0.672250 \n",
"569 -2.807598 0.429742 0.681303 2.110926 1.443053 -1.329212 2.269497 \n",
"\n",
" X0 X1 \n",
"273 -2.090940 -0.737435 \n",
"597 -0.106910 0.298973 \n",
"840 0.564455 -0.079447 \n",
"24 0.468185 -0.985369 \n",
"520 1.018526 -0.933725 \n",
".. ... ... \n",
"111 0.827795 -0.809487 \n",
"990 -0.101237 0.415439 \n",
"669 -0.891973 -2.208832 \n",
"935 0.672250 0.059029 \n",
"569 2.269497 -1.329212 \n",
"\n",
"[1000 rows x 9 columns], 'y': 273 -2.115646\n",
"597 -1.143963\n",
"840 15.748095\n",
"24 -7.244556\n",
"520 -5.145441\n",
" ... \n",
"111 16.735009\n",
"990 8.714440\n",
"669 6.961904\n",
"935 7.518694\n",
"569 16.043709\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 273 True\n",
"597 False\n",
"840 True\n",
"24 False\n",
"520 False\n",
" ... \n",
"111 True\n",
"990 True\n",
"669 True\n",
"935 True\n",
"569 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"712 -0.414558 0.791334 -0.285844 1.287638 -0.036979 -0.636977 -0.815817 \n",
"391 -0.372872 1.188105 1.332948 -1.096777 0.691069 0.171162 0.169036 \n",
"20 -1.011585 1.257003 -0.535764 1.111335 -0.895269 0.514914 1.672974 \n",
"470 -0.162220 1.935118 -0.011814 -0.925369 -2.133829 -0.933173 -1.040196 \n",
"314 -0.880874 -0.400956 -2.328729 0.380658 0.016809 -2.711706 1.361550 \n",
".. ... ... ... ... ... ... ... \n",
"775 -2.373011 1.748196 -1.426159 0.981177 -1.145867 -0.033774 0.340850 \n",
"610 -0.587750 1.485507 0.022402 0.026463 -1.269696 -0.553590 1.054406 \n",
"426 -0.510025 1.271371 -0.839987 -0.487718 0.846988 -0.539433 -0.390435 \n",
"512 -1.207671 1.719265 1.811797 3.102993 -1.534562 -1.029973 0.170662 \n",
"940 1.529456 -0.177965 -1.014976 0.360544 -0.351084 -1.262187 0.175720 \n",
"\n",
" X0 X1 \n",
"712 -0.815817 -0.636977 \n",
"391 0.169036 0.171162 \n",
"20 1.672974 0.514914 \n",
"470 -1.040196 -0.933173 \n",
"314 1.361550 -2.711706 \n",
".. ... ... \n",
"775 0.340850 -0.033774 \n",
"610 1.054406 -0.553590 \n",
"426 -0.390435 -0.539433 \n",
"512 0.170662 -1.029973 \n",
"940 0.175720 -1.262187 \n",
"\n",
"[1000 rows x 9 columns], 'y': 712 6.780320\n",
"391 13.502571\n",
"20 14.509975\n",
"470 -0.021734\n",
"314 -3.811119\n",
" ... \n",
"775 -7.934090\n",
"610 9.248446\n",
"426 8.947595\n",
"512 8.367137\n",
"940 12.297833\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 712 True\n",
"391 True\n",
"20 True\n",
"470 True\n",
"314 False\n",
" ... \n",
"775 False\n",
"610 True\n",
"426 True\n",
"512 True\n",
"940 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Control): 0.7374\n",
"INFO:causalml: Gini (Treatment): 0.9848\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0460\n",
"INFO:causalml: RMSE (Treatment): 0.9925\n",
"INFO:causalml: sMAPE (Control): 0.5278\n",
"INFO:causalml: sMAPE (Treatment): 0.1729\n",
"INFO:causalml: Gini (Control): 0.7357\n",
"INFO:causalml: Gini (Treatment): 0.9855\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"574 -0.628170 0.490047 -0.998970 1.114067 -0.634946 -0.977042 -0.135971 \n",
"279 -2.042524 -0.515422 -0.001494 0.585008 -0.007506 0.718590 1.220915 \n",
"4 -1.318715 0.653079 -2.000279 0.498075 -0.156856 -0.883068 0.497926 \n",
"346 -1.629199 0.177398 -0.054513 1.040503 0.789129 -0.768061 0.556015 \n",
"311 -0.500132 -0.676690 0.496760 -0.867403 -1.887667 0.095543 1.170573 \n",
".. ... ... ... ... ... ... ... \n",
"165 0.701052 -0.293887 1.099465 -2.275733 -1.154126 -1.597081 0.433060 \n",
"186 -0.713055 3.862460 0.200335 1.622113 -1.025908 -0.071922 0.803886 \n",
"432 -1.883946 0.320996 0.350803 0.548764 0.239354 -0.240906 0.080076 \n",
"363 -1.458807 1.554723 -0.100304 -0.726284 -0.183216 -2.651428 1.761662 \n",
"256 -0.520119 1.788996 -0.945605 0.029491 0.376885 -0.323118 2.874166 \n",
"\n",
" X0 X1 \n",
"574 -0.135971 -0.977042 \n",
"279 1.220915 0.718590 \n",
"4 0.497926 -0.883068 \n",
"346 0.556015 -0.768061 \n",
"311 1.170573 0.095543 \n",
".. ... ... \n",
"165 0.433060 -1.597081 \n",
"186 0.803886 -0.071922 \n",
"432 0.080076 -0.240906 \n",
"363 1.761662 -2.651428 \n",
"256 2.874166 -0.323118 \n",
"\n",
"[1000 rows x 9 columns], 'y': 574 5.633909\n",
"279 -6.155145\n",
"4 -4.253140\n",
"346 10.270986\n",
"311 6.548500\n",
" ... \n",
"165 4.362892\n",
"186 14.738530\n",
"432 6.046513\n",
"363 8.937908\n",
"256 20.538593\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 574 True\n",
"279 False\n",
"4 False\n",
"346 True\n",
"311 True\n",
" ... \n",
"165 True\n",
"186 True\n",
"432 True\n",
"363 True\n",
"256 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0604\n",
"INFO:causalml: RMSE (Treatment): 0.9944\n",
"INFO:causalml: sMAPE (Control): 0.5353\n",
"INFO:causalml: sMAPE (Treatment): 0.1628\n",
"INFO:causalml: Gini (Control): 0.7639\n",
"INFO:causalml: Gini (Treatment): 0.9878\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1445\n",
"INFO:causalml: RMSE (Treatment): 0.9943\n",
"INFO:causalml: sMAPE (Control): 0.5612\n",
"INFO:causalml: sMAPE (Treatment): 0.1686\n",
"INFO:causalml: Gini (Control): 0.7414\n",
"INFO:causalml: Gini (Treatment): 0.9876\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"589 -1.748176 -0.155602 -1.017759 -0.269662 -1.463838 0.574539 -0.268489 \n",
"34 0.308690 2.301209 0.550368 -0.173328 0.312406 1.777416 1.357787 \n",
"925 -0.454416 -0.506845 -1.768763 1.827165 -1.341544 -1.475720 -0.015125 \n",
"500 -0.845036 -0.731265 0.645794 1.354525 1.237255 -0.111407 1.537868 \n",
"646 -0.583674 1.079458 0.046224 1.641185 -1.743680 -0.669495 1.053700 \n",
".. ... ... ... ... ... ... ... \n",
"793 -0.493825 1.869213 -0.304250 -1.310964 -1.740684 -0.303164 1.395958 \n",
"189 0.331711 0.200259 1.290837 -0.775730 -3.102590 -0.388644 0.719845 \n",
"847 -0.727785 1.444691 0.778506 2.582550 -2.760931 -1.801513 -0.277971 \n",
"585 0.992384 0.557539 -0.314856 0.502749 0.252605 0.635638 0.314569 \n",
"557 0.221457 -0.260211 0.145958 1.385913 -1.584558 -1.443598 -0.270600 \n",
"\n",
" X0 X1 \n",
"589 -0.268489 0.574539 \n",
"34 1.357787 1.777416 \n",
"925 -0.015125 -1.475720 \n",
"500 1.537868 -0.111407 \n",
"646 1.053700 -0.669495 \n",
".. ... ... \n",
"793 1.395958 -0.303164 \n",
"189 0.719845 -0.388644 \n",
"847 -0.277971 -1.801513 \n",
"585 0.314569 0.635638 \n",
"557 -0.270600 -1.443598 \n",
"\n",
"[1000 rows x 9 columns], 'y': 589 -10.258505\n",
"34 22.166660\n",
"925 4.053751\n",
"500 17.859054\n",
"646 9.399353\n",
" ... \n",
"793 9.029191\n",
"189 5.702358\n",
"847 2.927082\n",
"585 17.683899\n",
"557 5.886610\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 589 False\n",
"34 True\n",
"925 True\n",
"500 True\n",
"646 True\n",
" ... \n",
"793 True\n",
"189 True\n",
"847 True\n",
"585 True\n",
"557 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"670 -0.277250 -0.210543 -0.138522 0.102428 0.056937 -0.175638 0.013252 \n",
"513 -0.744367 1.544602 -0.662760 2.360959 3.314790 -0.465990 0.294035 \n",
"566 -0.642524 0.944299 -1.173029 0.761402 -0.130403 -1.047601 0.808957 \n",
"82 -1.511905 0.757699 -0.695620 1.209082 -0.799840 -0.648411 -0.917466 \n",
"720 -0.172043 0.454702 -1.152236 -1.934944 -1.645816 0.040783 2.268147 \n",
".. ... ... ... ... ... ... ... \n",
"624 -1.991574 -0.338454 -0.082311 1.218307 -1.216017 0.236149 1.025945 \n",
"85 -1.707670 1.488772 -0.541956 -0.093565 -1.123841 -2.815247 -0.273263 \n",
"802 -0.876361 0.328210 1.791160 1.911211 -1.427104 -1.932645 0.590992 \n",
"256 -0.672850 1.743199 -0.845564 0.204138 0.135315 -0.465105 3.182018 \n",
"686 -1.425466 -0.586366 -0.500467 2.146743 -1.612155 0.644111 0.201290 \n",
"\n",
" X0 X1 \n",
"670 0.013252 -0.175638 \n",
"513 0.294035 -0.465990 \n",
"566 0.808957 -1.047601 \n",
"82 -0.917466 -0.648411 \n",
"720 2.268147 0.040783 \n",
".. ... ... \n",
"624 1.025945 0.236149 \n",
"85 -0.273263 -2.815247 \n",
"802 0.590992 -1.932645 \n",
"256 3.182018 -0.465105 \n",
"686 0.201290 0.644111 \n",
"\n",
"[1000 rows x 9 columns], 'y': 670 8.500958\n",
"513 22.411994\n",
"566 11.504404\n",
"82 0.633027\n",
"720 11.018665\n",
" ... \n",
"624 5.810979\n",
"85 -0.384362\n",
"802 7.188003\n",
"256 20.538593\n",
"686 4.432389\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 670 True\n",
"513 True\n",
"566 True\n",
"82 True\n",
"720 True\n",
" ... \n",
"624 True\n",
"85 True\n",
"802 True\n",
"256 True\n",
"686 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0158\n",
"INFO:causalml: RMSE (Treatment): 0.9613\n",
"INFO:causalml: sMAPE (Control): 0.5428\n",
"INFO:causalml: sMAPE (Treatment): 0.1827\n",
"INFO:causalml: Gini (Control): 0.7311\n",
"INFO:causalml: Gini (Treatment): 0.9887\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"294 0.652011 0.298007 -0.418953 0.292428 -0.732963 -1.633518 0.181559 \n",
"839 -0.412169 0.833947 -0.603844 0.891018 0.087956 -0.819805 0.059602 \n",
"397 0.740199 0.400858 -1.595990 -0.603329 -0.574721 -0.509353 0.696423 \n",
"838 -0.740006 -0.058434 -0.927145 0.910512 -0.777889 -1.034288 -0.481204 \n",
"473 -2.185995 3.010844 0.110059 0.565874 -1.589594 -2.013939 0.488150 \n",
".. ... ... ... ... ... ... ... \n",
"69 -1.483962 0.528791 -1.403685 1.730814 -1.962470 0.895590 2.864753 \n",
"308 -1.362757 0.359206 0.455134 0.677304 1.099989 -0.654966 0.653793 \n",
"585 0.879539 0.642240 -0.066938 0.411457 -0.063654 0.460095 0.641935 \n",
"60 0.288490 -0.246400 -0.063626 1.811323 -1.230966 1.056647 -0.921207 \n",
"878 -1.071925 2.714481 -2.157851 1.105897 -0.013140 -0.774942 0.812385 \n",
"\n",
" X0 X1 \n",
"294 0.181559 -1.633518 \n",
"839 0.059602 -0.819805 \n",
"397 0.696423 -0.509353 \n",
"838 -0.481204 -1.034288 \n",
"473 0.488150 -2.013939 \n",
".. ... ... \n",
"69 2.864753 0.895590 \n",
"308 0.653793 -0.654966 \n",
"585 0.641935 0.460095 \n",
"60 -0.921207 1.056647 \n",
"878 0.812385 -0.774942 \n",
"\n",
"[1000 rows x 9 columns], 'y': 294 8.885443\n",
"839 10.718157\n",
"397 12.505105\n",
"838 3.145267\n",
"473 2.964870\n",
" ... \n",
"69 14.310653\n",
"308 12.604981\n",
"585 17.683899\n",
"60 6.681242\n",
"878 11.079847\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 294 True\n",
"839 True\n",
"397 True\n",
"838 True\n",
"473 True\n",
" ... \n",
"69 True\n",
"308 True\n",
"585 True\n",
"60 True\n",
"878 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"528 -1.299357 2.085097 -0.391547 0.457171 -1.708455 0.655051 1.011679 \n",
"304 -0.683685 0.803297 1.483699 2.148691 0.979045 1.007386 1.610230 \n",
"681 -0.792822 1.511898 0.728455 -0.982430 1.296513 -0.075802 1.899100 \n",
"578 -0.435146 -0.528440 -0.535245 -0.909243 -0.449900 -0.174179 0.636845 \n",
"462 -1.007114 1.121916 -0.135373 -0.093987 0.835789 -0.091912 -0.269468 \n",
".. ... ... ... ... ... ... ... \n",
"292 -2.719957 1.122903 -0.402724 0.686720 -1.560917 0.788547 0.215067 \n",
"610 -0.710158 1.637766 0.101329 -0.160697 -1.425310 -0.568631 1.076851 \n",
"871 -1.592522 -0.440663 -2.352130 -0.994201 -0.403635 -0.702024 -0.783500 \n",
"133 -2.033771 3.169595 0.556234 0.158380 1.204623 0.987751 1.142136 \n",
"500 -0.779020 -0.811585 0.655207 1.303179 1.124806 -0.213754 1.466213 \n",
"\n",
" X0 X1 \n",
"528 1.011679 0.655051 \n",
"304 1.610230 1.007386 \n",
"681 1.899100 -0.075802 \n",
"578 0.636845 -0.174179 \n",
"462 -0.269468 -0.091912 \n",
".. ... ... \n",
"292 0.215067 0.788547 \n",
"610 1.076851 -0.568631 \n",
"871 -0.783500 -0.702024 \n",
"133 1.142136 0.987751 \n",
"500 1.466213 -0.213754 \n",
"\n",
"[1000 rows x 9 columns], 'y': 528 8.156158\n",
"304 22.259686\n",
"681 18.090454\n",
"578 -2.946371\n",
"462 9.112148\n",
" ... \n",
"292 -10.155301\n",
"610 9.248446\n",
"871 -8.791062\n",
"133 16.345538\n",
"500 17.859054\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 528 True\n",
"304 True\n",
"681 True\n",
"578 False\n",
"462 True\n",
" ... \n",
"292 False\n",
"610 True\n",
"871 False\n",
"133 True\n",
"500 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1122\n",
"INFO:causalml: RMSE (Treatment): 0.9994\n",
"INFO:causalml: sMAPE (Control): 0.5167\n",
"INFO:causalml: sMAPE (Treatment): 0.1760\n",
"INFO:causalml: Gini (Control): 0.7523\n",
"INFO:causalml: Gini (Treatment): 0.9873\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0266\n",
"INFO:causalml: RMSE (Treatment): 1.0330\n",
"INFO:causalml: sMAPE (Control): 0.5590\n",
"INFO:causalml: sMAPE (Treatment): 0.2011\n",
"INFO:causalml: Gini (Control): 0.7985\n",
"INFO:causalml: Gini (Treatment): 0.9884\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9877\n",
"INFO:causalml: RMSE (Treatment): 1.0373\n",
"INFO:causalml: sMAPE (Control): 0.5431\n",
"INFO:causalml: sMAPE (Treatment): 0.1665\n",
"INFO:causalml: Gini (Control): 0.7361\n",
"INFO:causalml: Gini (Treatment): 0.9850\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"72 -1.947483 -0.962961 -1.120901 -0.939085 -1.343606 -0.303198 0.063538 \n",
"503 -2.046887 1.940449 -1.561164 0.401595 0.508324 -0.415936 1.012491 \n",
"58 -0.709985 1.240568 0.630926 -0.459984 -0.993976 -0.669026 0.683581 \n",
"857 -0.857882 0.661107 -0.166046 -1.069666 -0.415578 -0.026838 -0.309020 \n",
"875 -2.420560 0.087914 0.299755 0.670579 -0.455537 0.289669 -0.663331 \n",
".. ... ... ... ... ... ... ... \n",
"101 -1.360162 -0.844073 -1.223409 -1.144797 -0.060488 -1.554531 -0.567665 \n",
"505 -2.457799 0.798147 0.680603 -0.221115 0.916495 -1.643212 1.350440 \n",
"1 -0.581011 0.661790 -1.094139 1.705350 -1.125393 0.910010 0.590032 \n",
"965 -0.112124 2.114344 -1.426137 0.314121 -0.771549 -0.548006 3.069445 \n",
"70 -0.733554 1.753741 -1.894252 -1.244993 0.873740 -0.934582 2.364144 \n",
"\n",
" X0 X1 \n",
"72 0.063538 -0.303198 \n",
"503 1.012491 -0.415936 \n",
"58 0.683581 -0.669026 \n",
"857 -0.309020 -0.026838 \n",
"875 -0.663331 0.289669 \n",
".. ... ... \n",
"101 -0.567665 -1.554531 \n",
"505 1.350440 -1.643212 \n",
"1 0.590032 0.910010 \n",
"965 3.069445 -0.548006 \n",
"70 2.364144 -0.934582 \n",
"\n",
"[1000 rows x 9 columns], 'y': 72 -12.586391\n",
"503 -3.163402\n",
"58 -3.753323\n",
"857 4.361211\n",
"875 1.205333\n",
" ... \n",
"101 -1.913847\n",
"505 8.711479\n",
"1 11.125450\n",
"965 19.735711\n",
"70 15.668385\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 72 False\n",
"503 False\n",
"58 False\n",
"857 True\n",
"875 True\n",
" ... \n",
"101 True\n",
"505 True\n",
"1 True\n",
"965 True\n",
"70 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"388 -1.737764 0.579226 -0.996179 0.571709 0.179947 0.265540 1.413841 \n",
"721 0.151053 -0.186372 -2.078046 0.489469 -0.482097 -1.560702 0.955690 \n",
"530 -0.970691 -0.230353 -1.122322 0.705896 -0.676484 -1.535906 1.770396 \n",
"375 -1.706684 -0.608418 -0.324004 -0.367281 -0.953427 -1.326523 1.412785 \n",
"795 -1.193762 1.727405 -1.310480 -0.078728 0.061590 0.338880 -0.823927 \n",
".. ... ... ... ... ... ... ... \n",
"784 -1.361975 0.925987 -1.238230 0.154941 -0.219589 2.112740 1.850467 \n",
"706 -0.579411 2.152504 0.049398 -0.124011 0.829518 -0.821826 2.406820 \n",
"452 -1.161144 1.447375 0.366940 1.502686 -0.832247 -2.464888 0.619286 \n",
"250 -2.076800 2.575995 -3.396775 0.000262 0.176806 -0.916428 -0.759909 \n",
"401 -1.900223 1.367527 -1.583811 1.389221 -0.270817 0.195192 0.439579 \n",
"\n",
" X0 X1 \n",
"388 1.413841 0.265540 \n",
"721 0.955690 -1.560702 \n",
"530 1.770396 -1.535906 \n",
"375 1.412785 -1.326523 \n",
"795 -0.823927 0.338880 \n",
".. ... ... \n",
"784 1.850467 2.112740 \n",
"706 2.406820 -0.821826 \n",
"452 0.619286 -2.464888 \n",
"250 -0.759909 -0.916428 \n",
"401 0.439579 0.195192 \n",
"\n",
"[1000 rows x 9 columns], 'y': 388 -4.782170\n",
"721 9.936280\n",
"530 10.101463\n",
"375 3.740574\n",
"795 5.028396\n",
" ... \n",
"784 14.827481\n",
"706 19.485830\n",
"452 7.577603\n",
"250 2.593009\n",
"401 7.798611\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 388 False\n",
"721 True\n",
"530 True\n",
"375 True\n",
"795 True\n",
" ... \n",
"784 True\n",
"706 True\n",
"452 True\n",
"250 True\n",
"401 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9472\n",
"INFO:causalml: RMSE (Treatment): 1.0280\n",
"INFO:causalml: sMAPE (Control): 0.5266\n",
"INFO:causalml: sMAPE (Treatment): 0.1841\n",
"INFO:causalml: Gini (Control): 0.7202\n",
"INFO:causalml: Gini (Treatment): 0.9863\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1008\n",
"INFO:causalml: RMSE (Treatment): 1.0163\n",
"INFO:causalml: sMAPE (Control): 0.5614\n",
"INFO:causalml: sMAPE (Treatment): 0.1746\n",
"INFO:causalml: Gini (Control): 0.7255\n",
"INFO:causalml: Gini (Treatment): 0.9873\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"575 -1.604112 1.420476 -0.119304 2.071894 -1.150966 -0.098560 -1.312888 \n",
"145 -2.738138 1.417467 -0.765806 0.714971 0.420035 0.129769 0.212606 \n",
"801 -0.175570 1.353509 -0.480641 -0.622820 1.012828 -1.661558 0.661143 \n",
"890 -1.229726 2.226561 -0.836272 -0.359287 -0.680700 -0.737701 -0.350118 \n",
"338 0.185464 2.399160 -0.341294 0.318392 0.001034 -1.870320 1.383703 \n",
".. ... ... ... ... ... ... ... \n",
"87 -1.335835 1.166508 -2.310867 1.215919 0.564484 0.080287 -0.101429 \n",
"396 -0.179289 -2.035594 -1.104917 -1.327889 -1.528992 0.540613 0.405879 \n",
"535 -0.407648 -0.494758 -0.724853 0.480031 -0.544243 -1.247736 2.925960 \n",
"853 -0.576296 1.875702 -0.953840 -2.121049 0.646644 0.852404 0.526995 \n",
"122 -2.744112 -0.432944 -1.275036 0.830403 0.123720 -1.720654 0.565414 \n",
"\n",
" X0 X1 \n",
"575 -1.312888 -0.098560 \n",
"145 0.212606 0.129769 \n",
"801 0.661143 -1.661558 \n",
"890 -0.350118 -0.737701 \n",
"338 1.383703 -1.870320 \n",
".. ... ... \n",
"87 -0.101429 0.080287 \n",
"396 0.405879 0.540613 \n",
"535 2.925960 -1.247736 \n",
"853 0.526995 0.852404 \n",
"122 0.565414 -1.720654 \n",
"\n",
"[1000 rows x 9 columns], 'y': 575 1.988673\n",
"145 -4.715614\n",
"801 13.424745\n",
"890 -3.809211\n",
"338 14.613827\n",
" ... \n",
"87 8.347375\n",
"396 -9.105144\n",
"535 16.319324\n",
"853 11.211181\n",
"122 -8.281570\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 575 True\n",
"145 False\n",
"801 True\n",
"890 False\n",
"338 True\n",
" ... \n",
"87 True\n",
"396 False\n",
"535 True\n",
"853 True\n",
"122 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"570 -0.251892 2.217378 0.128972 0.877789 -2.406003 -0.913432 -1.619846 \n",
"138 0.573441 0.791258 -0.393677 1.597789 -1.053529 0.436214 0.005347 \n",
"175 -1.239073 0.705045 -0.631863 0.820378 0.228619 -1.240018 2.829001 \n",
"177 -2.041704 0.339386 1.887360 0.853861 0.079121 -1.425156 2.631870 \n",
"507 -1.566631 -0.152512 -1.731438 0.974744 -1.418661 -1.855128 0.857973 \n",
".. ... ... ... ... ... ... ... \n",
"341 -1.380210 0.251500 2.155730 -1.460049 -0.465035 -1.399468 0.820430 \n",
"882 -2.200841 0.473423 -1.663119 -0.534963 0.991635 -1.095692 -0.001438 \n",
"52 0.272559 1.264574 0.920277 1.558250 -1.276324 0.140228 -0.269838 \n",
"126 -0.180520 2.109796 0.497949 -0.657129 -0.339303 -1.753526 0.177609 \n",
"191 -0.095375 -0.266770 -1.843198 0.164677 -1.451439 0.072266 0.011743 \n",
"\n",
" X0 X1 \n",
"570 -1.619846 -0.913432 \n",
"138 0.005347 0.436214 \n",
"175 2.829001 -1.240018 \n",
"177 2.631870 -1.425156 \n",
"507 0.857973 -1.855128 \n",
".. ... ... \n",
"341 0.820430 -1.399468 \n",
"882 -0.001438 -1.095692 \n",
"52 -0.269838 0.140228 \n",
"126 0.177609 -1.753526 \n",
"191 0.011743 0.072266 \n",
"\n",
"[1000 rows x 9 columns], 'y': 570 -0.557158\n",
"138 11.274922\n",
"175 17.721185\n",
"177 13.887378\n",
"507 -8.258449\n",
" ... \n",
"341 5.872196\n",
"882 -4.513230\n",
"52 9.973921\n",
"126 9.175909\n",
"191 -5.492128\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 570 True\n",
"138 True\n",
"175 True\n",
"177 True\n",
"507 False\n",
" ... \n",
"341 True\n",
"882 False\n",
"52 True\n",
"126 True\n",
"191 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8388\n",
"INFO:causalml: RMSE (Treatment): 0.9421\n",
"INFO:causalml: sMAPE (Control): 0.4387\n",
"INFO:causalml: sMAPE (Treatment): 0.1767\n",
"INFO:causalml: Gini (Control): 0.7289\n",
"INFO:causalml: Gini (Treatment): 0.9871\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"674 -1.275184 1.358200 -0.726155 -0.106801 1.792994 -0.003869 -0.927698 \n",
"170 -1.230840 2.762799 -1.489235 0.316969 -0.664481 -0.677436 -0.700865 \n",
"364 -1.093882 1.445454 1.445245 -0.311255 -1.621083 -0.424070 1.006841 \n",
"454 -0.414301 0.374911 -0.825270 0.696024 -1.912538 -0.252441 2.359192 \n",
"1 -0.546745 0.560050 -1.029776 1.825449 -1.022233 0.862300 0.474211 \n",
".. ... ... ... ... ... ... ... \n",
"301 -0.716979 -0.937502 0.498046 0.779203 -0.393113 -1.010865 1.063551 \n",
"209 -0.084613 0.739022 -1.232722 -0.059404 1.067833 -0.912770 -0.696018 \n",
"139 -1.718548 0.936408 0.545828 0.020241 0.924205 -0.693409 1.116505 \n",
"982 -0.800873 0.522693 -0.407466 -0.942755 0.556857 -1.488190 1.116656 \n",
"353 -0.057680 1.006295 -0.067730 -0.207153 -1.318915 0.302234 2.503398 \n",
"\n",
" X0 X1 \n",
"674 -0.927698 -0.003869 \n",
"170 -0.700865 -0.677436 \n",
"364 1.006841 -0.424070 \n",
"454 2.359192 -0.252441 \n",
"1 0.474211 0.862300 \n",
".. ... ... \n",
"301 1.063551 -1.010865 \n",
"209 -0.696018 -0.912770 \n",
"139 1.116505 -0.693409 \n",
"982 1.116656 -1.488190 \n",
"353 2.503398 0.302234 \n",
"\n",
"[1000 rows x 9 columns], 'y': 674 9.459822\n",
"170 -2.441764\n",
"364 7.693260\n",
"454 12.121477\n",
"1 11.125450\n",
" ... \n",
"301 9.391349\n",
"209 8.004465\n",
"139 11.778033\n",
"982 12.003112\n",
"353 16.031327\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 674 True\n",
"170 False\n",
"364 True\n",
"454 True\n",
"1 True\n",
" ... \n",
"301 True\n",
"209 True\n",
"139 True\n",
"982 True\n",
"353 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"996 -2.971085 1.947352 -0.453038 1.635119 1.145276 1.018412 2.208047 \n",
"524 -0.145506 0.713185 -0.266433 -0.617454 0.114717 -0.278091 2.170591 \n",
"450 0.200883 0.403165 0.126127 1.144775 -0.779232 -1.787856 -2.799749 \n",
"697 -0.708518 0.657108 -1.175912 0.534101 -0.825237 -2.467847 0.026325 \n",
"396 -0.268793 -2.167808 -1.130802 -1.329649 -1.476917 0.552037 0.543689 \n",
".. ... ... ... ... ... ... ... \n",
"370 -1.308630 0.430302 -1.112576 0.925546 -1.404983 -0.084853 0.940627 \n",
"918 -1.811025 1.249892 0.812562 0.987837 -0.536020 -0.154597 1.555279 \n",
"453 -1.161964 -0.627300 -0.312053 1.010994 -0.420745 -0.378774 -0.314639 \n",
"196 -2.030011 1.175683 -1.086168 -0.637678 -1.431378 -1.200674 0.660302 \n",
"73 -0.751418 0.109541 -0.967291 1.512617 -1.724470 -0.846512 3.628383 \n",
"\n",
" X0 X1 \n",
"996 2.208047 1.018412 \n",
"524 2.170591 -0.278091 \n",
"450 -2.799749 -1.787856 \n",
"697 0.026325 -2.467847 \n",
"396 0.543689 0.552037 \n",
".. ... ... \n",
"370 0.940627 -0.084853 \n",
"918 1.555279 -0.154597 \n",
"453 -0.314639 -0.378774 \n",
"196 0.660302 -1.200674 \n",
"73 3.628383 -0.846512 \n",
"\n",
"[1000 rows x 9 columns], 'y': 996 17.211685\n",
"524 18.575304\n",
"450 -2.499583\n",
"697 3.459264\n",
"396 -9.105144\n",
" ... \n",
"370 6.533270\n",
"918 12.845128\n",
"453 4.442757\n",
"196 1.676450\n",
"73 16.038810\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 996 True\n",
"524 True\n",
"450 True\n",
"697 True\n",
"396 False\n",
" ... \n",
"370 True\n",
"918 True\n",
"453 True\n",
"196 True\n",
"73 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Control): 3.1302\n",
"INFO:causalml: RMSE (Treatment): 0.9367\n",
"INFO:causalml: sMAPE (Control): 0.5797\n",
"INFO:causalml: sMAPE (Treatment): 0.1733\n",
"INFO:causalml: Gini (Control): 0.7355\n",
"INFO:causalml: Gini (Treatment): 0.9891\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0294\n",
"INFO:causalml: RMSE (Treatment): 0.9617\n",
"INFO:causalml: sMAPE (Control): 0.5461\n",
"INFO:causalml: sMAPE (Treatment): 0.1705\n",
"INFO:causalml: Gini (Control): 0.7490\n",
"INFO:causalml: Gini (Treatment): 0.9879\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1241\n",
"INFO:causalml: RMSE (Treatment): 1.0540\n",
"INFO:causalml: sMAPE (Control): 0.4882\n",
"INFO:causalml: sMAPE (Treatment): 0.1936\n",
"INFO:causalml: Gini (Control): 0.7337\n",
"INFO:causalml: Gini (Treatment): 0.9856\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"75 -0.757333 -0.712916 0.105189 -0.080202 -0.707051 1.598623 2.093872 \n",
"102 -0.409235 -0.541200 -1.639509 1.664483 0.141387 -1.093047 0.767702 \n",
"814 0.343624 2.428392 -0.183959 0.104039 -0.569434 -1.234335 -0.143349 \n",
"709 0.787223 0.949325 1.536416 0.009398 -0.989808 -1.695050 1.432090 \n",
"538 -1.220204 2.286013 0.177264 1.391621 1.819995 -2.184618 0.889050 \n",
".. ... ... ... ... ... ... ... \n",
"898 -1.313018 1.847124 -0.969984 1.714070 0.632784 -1.628770 2.241001 \n",
"976 0.092232 0.422641 -0.635967 -0.206560 0.383167 -1.439808 -0.728771 \n",
"432 -1.903219 0.642305 0.263048 0.563384 0.242865 -0.227973 0.052633 \n",
"989 -1.398945 1.910521 2.088727 0.018377 -1.371115 0.390374 1.342492 \n",
"534 -2.308233 -0.613270 -3.890913 -0.742875 -0.127011 -1.237730 0.209115 \n",
"\n",
" X0 X1 \n",
"75 2.093872 1.598623 \n",
"102 0.767702 -1.093047 \n",
"814 -0.143349 -1.234335 \n",
"709 1.432090 -1.695050 \n",
"538 0.889050 -2.184618 \n",
".. ... ... \n",
"898 2.241001 -1.628770 \n",
"976 -0.728771 -1.439808 \n",
"432 0.052633 -0.227973 \n",
"989 1.342492 0.390374 \n",
"534 0.209115 -1.237730 \n",
"\n",
"[1000 rows x 9 columns], 'y': 75 -4.842126\n",
"102 12.024923\n",
"814 8.970856\n",
"709 14.547782\n",
"538 16.761939\n",
" ... \n",
"898 17.331546\n",
"976 5.822048\n",
"432 6.046513\n",
"989 11.211139\n",
"534 -10.998817\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 75 False\n",
"102 True\n",
"814 True\n",
"709 True\n",
"538 True\n",
" ... \n",
"898 True\n",
"976 True\n",
"432 True\n",
"989 True\n",
"534 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"325 -0.989731 0.780426 -0.471838 0.421229 -0.502973 -1.145870 -1.010960 \n",
"816 -0.350574 0.965547 -0.381352 -0.638433 -2.333646 -1.730109 -0.276716 \n",
"556 -1.366154 0.375266 -0.654065 -1.339450 -0.190017 -2.154392 -0.115430 \n",
"769 -1.938171 0.734708 -0.308602 -0.203508 -0.249352 0.168703 1.681798 \n",
"227 -0.762855 1.853916 -0.703500 1.230172 0.001538 -1.531708 0.454908 \n",
".. ... ... ... ... ... ... ... \n",
"536 -1.368469 0.234808 -0.581402 -0.272878 -0.204108 1.257436 -1.011244 \n",
"585 0.915123 0.401585 -0.227586 0.532163 -0.063974 0.580786 0.861562 \n",
"331 -1.142880 1.570995 -1.097257 0.688392 0.264632 -1.329392 0.596813 \n",
"913 -1.821448 0.201364 -0.282816 2.146074 0.067142 -0.281985 0.904230 \n",
"67 -0.957141 0.080458 -0.232750 0.945741 -0.507281 -2.479363 -1.488055 \n",
"\n",
" X0 X1 \n",
"325 -1.010960 -1.145870 \n",
"816 -0.276716 -1.730109 \n",
"556 -0.115430 -2.154392 \n",
"769 1.681798 0.168703 \n",
"227 0.454908 -1.531708 \n",
".. ... ... \n",
"536 -1.011244 1.257436 \n",
"585 0.861562 0.580786 \n",
"331 0.596813 -1.329392 \n",
"913 0.904230 -0.281985 \n",
"67 -1.488055 -2.479363 \n",
"\n",
"[1000 rows x 9 columns], 'y': 325 1.470513\n",
"816 -0.020121\n",
"556 0.746452\n",
"769 -5.821681\n",
"227 9.555874\n",
" ... \n",
"536 3.123954\n",
"585 17.683899\n",
"331 10.231377\n",
"913 10.638397\n",
"67 -0.972001\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 325 True\n",
"816 True\n",
"556 True\n",
"769 False\n",
"227 True\n",
" ... \n",
"536 True\n",
"585 True\n",
"331 True\n",
"913 True\n",
"67 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9356\n",
"INFO:causalml: RMSE (Treatment): 0.9295\n",
"INFO:causalml: sMAPE (Control): 0.4843\n",
"INFO:causalml: sMAPE (Treatment): 0.1740\n",
"INFO:causalml: Gini (Control): 0.7237\n",
"INFO:causalml: Gini (Treatment): 0.9879\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8735\n",
"INFO:causalml: RMSE (Treatment): 0.9463\n",
"INFO:causalml: sMAPE (Control): 0.4996\n",
"INFO:causalml: sMAPE (Treatment): 0.2070\n",
"INFO:causalml: Gini (Control): 0.7439\n",
"INFO:causalml: Gini (Treatment): 0.9887\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"864 -1.378966 1.163425 -1.222624 -1.350334 -1.365408 -0.717210 0.466981 \n",
"87 -1.256415 1.284809 -2.247451 1.167455 0.624770 -0.131766 0.037739 \n",
"735 -0.714745 0.352892 -2.026534 -0.469867 0.867790 -1.244862 2.108344 \n",
"970 -1.482619 1.751589 1.381131 -0.789499 -0.669645 -1.595394 -0.017023 \n",
"872 -0.693148 1.474590 0.515663 -1.858463 0.370774 0.060584 0.865481 \n",
".. ... ... ... ... ... ... ... \n",
"425 -0.549250 0.962562 0.366956 0.492343 -0.240984 -1.775844 1.552650 \n",
"916 -2.362087 0.484083 -0.342292 0.332559 0.423326 0.218083 -0.177975 \n",
"85 -1.540931 1.480379 -0.472548 -0.111599 -0.965064 -2.732768 -0.265116 \n",
"357 -0.515377 -0.879357 -0.706456 -0.752520 -0.279704 0.325564 -0.580021 \n",
"216 -1.361145 1.753494 -2.439841 -1.078344 -0.815397 -1.262698 -0.057730 \n",
"\n",
" X0 X1 \n",
"864 0.466981 -0.717210 \n",
"87 0.037739 -0.131766 \n",
"735 2.108344 -1.244862 \n",
"970 -0.017023 -1.595394 \n",
"872 0.865481 0.060584 \n",
".. ... ... \n",
"425 1.552650 -1.775844 \n",
"916 -0.177975 0.218083 \n",
"85 -0.265116 -2.732768 \n",
"357 -0.580021 0.325564 \n",
"216 -0.057730 -1.262698 \n",
"\n",
"[1000 rows x 9 columns], 'y': 864 -8.539115\n",
"87 8.347375\n",
"735 15.212283\n",
"970 3.837965\n",
"872 11.901044\n",
" ... \n",
"425 13.323890\n",
"916 4.529654\n",
"85 -0.384362\n",
"357 4.629609\n",
"216 2.376140\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 864 False\n",
"87 True\n",
"735 True\n",
"970 True\n",
"872 True\n",
" ... \n",
"425 True\n",
"916 True\n",
"85 True\n",
"357 True\n",
"216 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"816 -0.388047 0.924588 -0.476783 -1.002072 -2.281807 -1.966699 -0.420618 \n",
"275 -2.213287 -0.386113 0.142531 1.807303 -0.624429 -1.486348 -1.323718 \n",
"999 -1.667723 0.404085 -0.014222 0.372220 -1.571434 -0.354246 -0.497524 \n",
"404 0.251516 -1.381578 -0.688007 1.323307 -0.512810 0.842583 -0.338387 \n",
"213 -0.847340 1.190321 -0.327402 1.416738 -1.672036 -0.540066 0.231177 \n",
".. ... ... ... ... ... ... ... \n",
"891 -0.268138 -0.445246 -0.878929 1.526581 0.791018 0.110399 1.971061 \n",
"247 -1.091098 0.094910 0.672182 0.926585 -0.317620 -1.799480 -0.591769 \n",
"762 0.248716 0.722264 -0.871655 0.522479 -0.891556 0.275079 2.329206 \n",
"110 -1.976096 2.523392 -0.612924 1.400103 -0.539657 0.638363 0.916249 \n",
"398 -0.408833 1.392336 -1.118775 0.410265 -1.701998 -1.404167 0.136762 \n",
"\n",
" X0 X1 \n",
"816 -0.420618 -1.966699 \n",
"275 -1.323718 -1.486348 \n",
"999 -0.497524 -0.354246 \n",
"404 -0.338387 0.842583 \n",
"213 0.231177 -0.540066 \n",
".. ... ... \n",
"891 1.971061 0.110399 \n",
"247 -0.591769 -1.799480 \n",
"762 2.329206 0.275079 \n",
"110 0.916249 0.638363 \n",
"398 0.136762 -1.404167 \n",
"\n",
"[1000 rows x 9 columns], 'y': 816 -0.020121\n",
"275 -1.163599\n",
"999 -8.100697\n",
"404 -1.323791\n",
"213 5.306177\n",
" ... \n",
"891 20.166272\n",
"247 3.320266\n",
"762 17.175897\n",
"110 11.934711\n",
"398 -5.105764\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 816 True\n",
"275 True\n",
"999 False\n",
"404 False\n",
"213 True\n",
" ... \n",
"891 True\n",
"247 True\n",
"762 True\n",
"110 True\n",
"398 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"26 0.023678 0.058490 0.106981 2.666127 -0.511525 -1.991283 2.058464 \n",
"631 0.511913 2.280563 -1.197253 1.562609 -0.215902 -1.020548 0.298896 \n",
"591 -1.917706 1.021035 1.482307 0.291711 -0.439067 -0.077290 -0.568812 \n",
"936 -0.469196 -1.072774 -0.445154 -0.658563 -0.924928 -2.700354 1.236708 \n",
"221 -1.078034 0.828821 -1.093313 -0.510332 -2.243030 1.374379 0.571832 \n",
".. ... ... ... ... ... ... ... \n",
"231 -1.059865 1.882541 -0.203531 0.417511 1.029628 -1.972507 1.683474 \n",
"45 -0.813366 0.618802 0.665742 -0.174151 -1.195950 -0.202764 1.213446 \n",
"897 -0.522876 -0.893288 -1.382943 0.031426 -0.364126 -0.181860 -0.365989 \n",
"79 -0.920979 0.756543 0.729420 0.835588 -0.316096 -0.910696 0.700811 \n",
"338 0.143678 2.424775 -0.366023 0.065665 -0.229007 -1.673547 1.227384 \n",
"\n",
" X0 X1 \n",
"26 2.058464 -1.991283 \n",
"631 0.298896 -1.020548 \n",
"591 -0.568812 -0.077290 \n",
"936 1.236708 -2.700354 \n",
"221 0.571832 1.374379 \n",
".. ... ... \n",
"231 1.683474 -1.972507 \n",
"45 1.213446 -0.202764 \n",
"897 -0.365989 -0.181860 \n",
"79 0.700811 -0.910696 \n",
"338 1.227384 -1.673547 \n",
"\n",
"[1000 rows x 9 columns], 'y': 26 17.468518\n",
"631 14.220450\n",
"591 3.407731\n",
"936 4.998172\n",
"221 -10.175373\n",
" ... \n",
"231 16.734242\n",
"45 9.275797\n",
"897 -4.566144\n",
"79 10.632327\n",
"338 14.613827\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 26 True\n",
"631 True\n",
"591 True\n",
"936 True\n",
"221 False\n",
" ... \n",
"231 True\n",
"45 True\n",
"897 False\n",
"79 True\n",
"338 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8528\n",
"INFO:causalml: RMSE (Treatment): 0.9856\n",
"INFO:causalml: sMAPE (Control): 0.4977\n",
"INFO:causalml: sMAPE (Treatment): 0.1629\n",
"INFO:causalml: Gini (Control): 0.7488\n",
"INFO:causalml: Gini (Treatment): 0.9862\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.2241\n",
"INFO:causalml: RMSE (Treatment): 0.9317\n",
"INFO:causalml: sMAPE (Control): 0.5776\n",
"INFO:causalml: sMAPE (Treatment): 0.1543\n",
"INFO:causalml: Gini (Control): 0.6994\n",
"INFO:causalml: Gini (Treatment): 0.9888\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"663 1.078179 0.156226 0.446981 -0.052989 -0.182723 0.688062 0.799450 \n",
"959 -1.315478 1.172398 -1.866088 0.694998 0.951762 1.571519 -1.076170 \n",
"542 0.662810 2.174263 -1.541615 1.123954 -0.612563 0.523472 -0.491306 \n",
"213 -1.155594 1.235499 -0.292570 1.237619 -1.615249 -0.939958 0.070592 \n",
"625 -1.321971 0.223750 0.478791 -0.019464 1.090621 -0.805170 1.259184 \n",
".. ... ... ... ... ... ... ... \n",
"9 0.192989 0.814800 1.424924 -1.145901 -0.763888 0.669395 -0.203615 \n",
"19 -0.477455 -0.356164 -0.890055 2.177785 -1.006223 -1.615244 0.088047 \n",
"830 -0.723356 -0.566580 0.276901 2.035714 -0.231138 -1.251536 1.154490 \n",
"343 -1.264323 1.803949 0.915137 -1.755312 -0.898431 -0.865052 0.358412 \n",
"818 -2.114619 0.956673 0.047994 0.222962 -2.378329 -0.680649 1.045604 \n",
"\n",
" X0 X1 \n",
"663 0.799450 0.688062 \n",
"959 -1.076170 1.571519 \n",
"542 -0.491306 0.523472 \n",
"213 0.070592 -0.939958 \n",
"625 1.259184 -0.805170 \n",
".. ... ... \n",
"9 -0.203615 0.669395 \n",
"19 0.088047 -1.615244 \n",
"830 1.154490 -1.251536 \n",
"343 0.358412 -0.865052 \n",
"818 1.045604 -0.680649 \n",
"\n",
"[1000 rows x 9 columns], 'y': 663 15.384533\n",
"959 7.562645\n",
"542 12.169721\n",
"213 5.306177\n",
"625 12.619755\n",
" ... \n",
"9 8.755592\n",
"19 6.700542\n",
"830 -1.062782\n",
"343 4.731928\n",
"818 -11.770683\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 663 True\n",
"959 True\n",
"542 True\n",
"213 True\n",
"625 True\n",
" ... \n",
"9 True\n",
"19 True\n",
"830 False\n",
"343 True\n",
"818 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"835 0.306562 -0.032909 -0.403386 -1.002239 1.594554 0.150366 1.873825 \n",
"630 -1.828867 0.706688 -0.076602 -1.285112 0.444461 1.199891 1.358834 \n",
"770 -2.884794 0.052229 -1.146703 -0.461669 -1.249223 0.455205 0.867935 \n",
"338 0.066256 2.294859 -0.250545 0.034695 -0.068221 -1.912920 1.239046 \n",
"574 -0.511164 0.093076 -1.210577 1.056846 -0.482827 -1.004801 -0.190245 \n",
".. ... ... ... ... ... ... ... \n",
"336 -3.046083 1.587357 0.558987 1.342059 -0.764320 -2.113244 0.925285 \n",
"95 -0.079302 4.714852 0.818300 0.292822 -0.515397 -2.804820 -0.486523 \n",
"713 -0.536668 2.005241 -1.347787 1.364096 -0.445617 -0.789056 -0.185889 \n",
"97 -0.694538 -0.132024 0.177391 0.442854 0.698154 0.802542 1.319311 \n",
"375 -1.959415 -0.308475 -0.556457 -0.497601 -0.924967 -1.428880 1.441709 \n",
"\n",
" X0 X1 \n",
"835 1.873825 0.150366 \n",
"630 1.358834 1.199891 \n",
"770 0.867935 0.455205 \n",
"338 1.239046 -1.912920 \n",
"574 -0.190245 -1.004801 \n",
".. ... ... \n",
"336 0.925285 -2.113244 \n",
"95 -0.486523 -2.804820 \n",
"713 -0.185889 -0.789056 \n",
"97 1.319311 0.802542 \n",
"375 1.441709 -1.428880 \n",
"\n",
"[1000 rows x 9 columns], 'y': 835 20.490073\n",
"630 11.111329\n",
"770 -12.067607\n",
"338 14.613827\n",
"574 5.633909\n",
" ... \n",
"336 2.926169\n",
"95 9.600613\n",
"713 9.135870\n",
"97 16.738796\n",
"375 3.740574\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 835 True\n",
"630 True\n",
"770 False\n",
"338 True\n",
"574 True\n",
" ... \n",
"336 True\n",
"95 True\n",
"713 True\n",
"97 True\n",
"375 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9748\n",
"INFO:causalml: RMSE (Treatment): 1.0791\n",
"INFO:causalml: sMAPE (Control): 0.5030\n",
"INFO:causalml: sMAPE (Treatment): 0.1791\n",
"INFO:causalml: Gini (Control): 0.7130\n",
"INFO:causalml: Gini (Treatment): 0.9842\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8029\n",
"INFO:causalml: RMSE (Treatment): 0.9547\n",
"INFO:causalml: sMAPE (Control): 0.5077\n",
"INFO:causalml: sMAPE (Treatment): 0.1519\n",
"INFO:causalml: Gini (Control): 0.7321\n",
"INFO:causalml: Gini (Treatment): 0.9868\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9671\n",
"INFO:causalml: RMSE (Treatment): 1.0479\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"349 -1.721742 0.994178 -0.248258 0.170946 0.084045 -0.579222 1.403727 \n",
"348 -1.015222 2.066811 -1.843225 0.417722 -1.444937 -1.728400 1.654515 \n",
"243 -3.048965 -0.518166 0.249463 1.413264 -1.336714 -0.364473 0.627325 \n",
"561 -0.585552 -0.209026 -0.357597 1.027069 -2.551457 0.321784 1.257139 \n",
"445 -0.080532 0.021305 0.997780 2.976259 -0.609740 -0.540314 1.162446 \n",
".. ... ... ... ... ... ... ... \n",
"898 -1.390026 1.825700 -0.988322 1.716423 0.559838 -1.567219 2.137330 \n",
"563 -0.466899 -0.165808 -3.015552 1.193963 -1.463896 1.092221 0.453459 \n",
"573 0.212725 1.077798 -1.054672 1.956676 -1.734482 -1.164064 -1.007024 \n",
"315 -0.646043 0.204124 1.405069 1.427420 0.253353 -0.779818 0.430896 \n",
"416 -1.034436 0.322248 -0.198104 -0.459426 -0.633344 -1.344082 -0.073182 \n",
"\n",
" X0 X1 \n",
"349 1.403727 -0.579222 \n",
"348 1.654515 -1.728400 \n",
"243 0.627325 -0.364473 \n",
"561 1.257139 0.321784 \n",
"445 1.162446 -0.540314 \n",
".. ... ... \n",
"898 2.137330 -1.567219 \n",
"563 0.453459 1.092221 \n",
"573 -1.007024 -1.164064 \n",
"315 0.430896 -0.779818 \n",
"416 -0.073182 -1.344082 \n",
"\n",
"[1000 rows x 9 columns], 'y': 349 10.315484\n",
"348 9.469476\n",
"243 -10.483545\n",
"561 8.271140\n",
"445 16.120442\n",
" ... \n",
"898 17.331546\n",
"563 -5.142521\n",
"573 3.364299\n",
"315 11.545213\n",
"416 2.647910\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 349 True\n",
"348 True\n",
"243 False\n",
"561 True\n",
"445 True\n",
" ... \n",
"898 True\n",
"563 False\n",
"573 True\n",
"315 True\n",
"416 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"177 -1.922237 0.485077 1.631578 0.819498 0.107530 -1.224345 2.252789 \n",
"248 -0.342131 0.605659 -1.673302 0.453481 1.089323 -0.869957 -0.334178 \n",
"519 -1.082507 1.630724 -0.037752 0.137577 -1.415357 -0.872408 0.516459 \n",
"54 1.568927 2.117927 1.208255 0.531668 -1.185251 -0.768777 -0.675083 \n",
"993 -0.932022 0.529658 -0.377867 0.116624 -0.725836 0.124112 1.236988 \n",
".. ... ... ... ... ... ... ... \n",
"698 -1.138849 1.739492 0.295478 1.372403 -1.312977 0.210428 0.045664 \n",
"135 -1.665508 2.299056 -1.063216 1.214164 -3.322477 -0.037896 0.924240 \n",
"681 -0.842081 1.444749 0.741345 -0.852470 1.257753 -0.079214 1.672446 \n",
"535 -0.646823 -0.444539 -0.586156 0.504034 -0.294741 -1.590632 3.031652 \n",
"135 -1.574404 2.295451 -1.082528 1.400681 -3.205474 0.187770 0.662262 \n",
"\n",
" X0 X1 \n",
"177 2.252789 -1.224345 \n",
"248 -0.334178 -0.869957 \n",
"519 0.516459 -0.872408 \n",
"54 -0.675083 -0.768777 \n",
"993 1.236988 0.124112 \n",
".. ... ... \n",
"698 0.045664 0.210428 \n",
"135 0.924240 -0.037896 \n",
"681 1.672446 -0.079214 \n",
"535 3.031652 -1.590632 \n",
"135 0.662262 0.187770 \n",
"\n",
"[1000 rows x 9 columns], 'y': 177 13.887378\n",
"248 10.511960\n",
"519 6.513308\n",
"54 9.737109\n",
"993 10.904694\n",
" ... \n",
"698 7.381116\n",
"135 2.418843\n",
"681 18.090454\n",
"535 16.319324\n",
"135 2.418843\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 177 True\n",
"248 True\n",
"519 True\n",
"54 True\n",
"993 True\n",
" ... \n",
"698 True\n",
"135 True\n",
"681 True\n",
"535 True\n",
"135 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Control): 0.5210\n",
"INFO:causalml: sMAPE (Treatment): 0.1817\n",
"INFO:causalml: Gini (Control): 0.7762\n",
"INFO:causalml: Gini (Treatment): 0.9860\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8423\n",
"INFO:causalml: RMSE (Treatment): 0.9524\n",
"INFO:causalml: sMAPE (Control): 0.4624\n",
"INFO:causalml: sMAPE (Treatment): 0.1790\n",
"INFO:causalml: Gini (Control): 0.7398\n",
"INFO:causalml: Gini (Treatment): 0.9876\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9004\n",
"INFO:causalml: RMSE (Treatment): 0.9385\n",
"INFO:causalml: sMAPE (Control): 0.4947\n",
"INFO:causalml: sMAPE (Treatment): 0.1545\n",
"INFO:causalml: Gini (Control): 0.7617\n",
"INFO:causalml: Gini (Treatment): 0.9878\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"724 -1.438144 -0.332737 0.392911 0.212140 -0.665798 0.343149 0.757040 \n",
"751 -0.401081 -0.175121 -0.010361 -0.048220 -0.675733 -1.009049 1.318978 \n",
"687 0.182106 0.577860 -1.602666 1.575414 -0.252950 -0.201800 2.081642 \n",
"294 0.691661 0.305707 -0.276849 0.436226 -0.938440 -1.498923 -0.104459 \n",
"100 -1.136832 1.936971 -0.818190 2.375104 -0.480383 -1.567064 1.095914 \n",
".. ... ... ... ... ... ... ... \n",
"311 -0.539638 -0.734239 0.330032 -0.759054 -1.973271 -0.025732 1.392467 \n",
"92 -1.274988 0.222087 -0.171338 0.406686 0.299645 -1.318381 0.211155 \n",
"280 -0.980919 -0.636800 -0.822122 -0.528152 -0.459369 -1.805989 1.034790 \n",
"948 0.160932 0.722128 0.042512 -0.375004 -1.753827 -0.357351 0.838413 \n",
"668 -0.174736 0.461147 0.483940 -0.073567 -1.070415 -0.771726 1.044764 \n",
"\n",
" X0 X1 \n",
"724 0.757040 0.343149 \n",
"751 1.318978 -1.009049 \n",
"687 2.081642 -0.201800 \n",
"294 -0.104459 -1.498923 \n",
"100 1.095914 -1.567064 \n",
".. ... ... \n",
"311 1.392467 -0.025732 \n",
"92 0.211155 -1.318381 \n",
"280 1.034790 -1.805989 \n",
"948 0.838413 -0.357351 \n",
"668 1.044764 -0.771726 \n",
"\n",
"[1000 rows x 9 columns], 'y': 724 7.856564\n",
"751 10.177478\n",
"687 19.701253\n",
"294 8.885443\n",
"100 11.113321\n",
" ... \n",
"311 6.548500\n",
"92 7.646588\n",
"280 5.994137\n",
"948 9.814477\n",
"668 10.579344\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 724 True\n",
"751 True\n",
"687 True\n",
"294 True\n",
"100 True\n",
" ... \n",
"311 True\n",
"92 True\n",
"280 True\n",
"948 True\n",
"668 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"51 -0.271968 0.458390 1.267380 -0.199087 -0.197657 -0.538826 -0.226384 \n",
"10 0.970625 3.067773 0.223898 2.879977 0.834710 -1.322450 1.055600 \n",
"975 -0.912818 0.808119 0.283279 1.816216 -0.525104 -1.245342 0.897152 \n",
"290 0.454061 -1.064971 0.315819 -0.383260 0.775556 -1.332200 0.745180 \n",
"642 -1.271036 0.321130 -1.184572 0.813957 -0.373138 0.408561 -0.441891 \n",
".. ... ... ... ... ... ... ... \n",
"23 -1.745951 0.284976 -0.584939 0.009987 0.281503 0.464791 2.089986 \n",
"905 -1.137892 0.810218 0.339671 -0.323423 -0.825219 -1.534036 0.640463 \n",
"216 -1.098435 1.733659 -2.455863 -0.949506 -0.900628 -1.484929 -0.096884 \n",
"28 0.519413 -1.793534 -0.629775 -1.531616 -0.133148 -0.455310 -0.300663 \n",
"741 -0.663581 2.882579 -1.455952 -0.962903 -1.235194 -0.395429 0.136463 \n",
"\n",
" X0 X1 \n",
"51 -0.226384 -0.538826 \n",
"10 1.055600 -1.322450 \n",
"975 0.897152 -1.245342 \n",
"290 0.745180 -1.332200 \n",
"642 -0.441891 0.408561 \n",
".. ... ... \n",
"23 2.089986 0.464791 \n",
"905 0.640463 -1.534036 \n",
"216 -0.096884 -1.484929 \n",
"28 -0.300663 -0.455310 \n",
"741 0.136463 -0.395429 \n",
"\n",
"[1000 rows x 9 columns], 'y': 51 8.374283\n",
"10 24.500212\n",
"975 9.860888\n",
"290 12.664366\n",
"642 4.413860\n",
" ... \n",
"23 -4.601417\n",
"905 -5.143310\n",
"216 2.376140\n",
"28 4.753158\n",
"741 5.593102\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 51 True\n",
"10 True\n",
"975 True\n",
"290 True\n",
"642 True\n",
" ... \n",
"23 False\n",
"905 False\n",
"216 True\n",
"28 True\n",
"741 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9230\n",
"INFO:causalml: RMSE (Treatment): 0.9689\n",
"INFO:causalml: sMAPE (Control): 0.5764\n",
"INFO:causalml: sMAPE (Treatment): 0.1908\n",
"INFO:causalml: Gini (Control): 0.7373\n",
"INFO:causalml: Gini (Treatment): 0.9894\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9404\n",
"INFO:causalml: RMSE (Treatment): 0.9361\n",
"INFO:causalml: sMAPE (Control): 0.5608\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"151 -1.451343 2.117412 0.057350 0.228720 -0.666035 0.318744 -0.507604 \n",
"289 0.709237 -0.097306 -0.534428 0.454660 1.091187 -1.461089 1.641729 \n",
"856 -2.090114 0.977529 -2.008539 -0.850157 -2.154341 -2.185380 -0.444760 \n",
"507 -1.498719 -0.280197 -1.843650 1.012497 -1.428442 -2.318102 0.960008 \n",
"593 -0.254514 1.059549 -0.632799 -1.430683 -0.380345 -0.097567 0.396568 \n",
".. ... ... ... ... ... ... ... \n",
"188 1.981869 1.294382 -0.672061 1.002822 -0.906319 0.110632 0.519760 \n",
"274 -1.441295 1.166923 -1.110185 1.572215 -1.119379 -0.864990 0.912239 \n",
"688 -0.547513 0.834360 -1.085616 -0.343214 -1.414722 -2.185953 0.446973 \n",
"173 -1.768526 0.219728 -1.886426 1.139153 -1.269672 -1.316015 0.551071 \n",
"365 -0.013680 -0.244481 0.764536 0.988104 0.414957 -1.574025 1.368041 \n",
"\n",
" X0 X1 \n",
"151 -0.507604 0.318744 \n",
"289 1.641729 -1.461089 \n",
"856 -0.444760 -2.185380 \n",
"507 0.960008 -2.318102 \n",
"593 0.396568 -0.097567 \n",
".. ... ... \n",
"188 0.519760 0.110632 \n",
"274 0.912239 -0.864990 \n",
"688 0.446973 -2.185953 \n",
"173 0.551071 -1.316015 \n",
"365 1.368041 -1.574025 \n",
"\n",
"[1000 rows x 9 columns], 'y': 151 4.957949\n",
"289 19.767563\n",
"856 -13.300039\n",
"507 -8.258449\n",
"593 8.505994\n",
" ... \n",
"188 17.138200\n",
"274 -4.605752\n",
"688 -5.466406\n",
"173 -7.857862\n",
"365 15.195618\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 151 True\n",
"289 True\n",
"856 False\n",
"507 False\n",
"593 True\n",
" ... \n",
"188 True\n",
"274 False\n",
"688 False\n",
"173 False\n",
"365 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"255 1.133141 2.240067 1.125542 -0.273125 -0.989053 -0.952641 0.326860 \n",
"574 -0.627392 0.302338 -0.988008 1.136665 -0.486729 -1.072095 -0.240808 \n",
"571 0.036812 -0.336157 1.466677 0.344865 0.441510 -0.684800 1.410161 \n",
"914 0.028245 1.206804 0.219451 -0.591808 -0.976701 0.822046 0.977400 \n",
"602 -2.005434 2.564907 -1.005840 1.727996 -1.563756 0.382768 -1.360408 \n",
".. ... ... ... ... ... ... ... \n",
"835 0.087010 0.143228 -0.435516 -1.147568 1.888092 0.346188 1.742445 \n",
"622 -0.937723 -0.820100 0.087610 -0.325510 0.338565 -0.135640 0.278474 \n",
"425 -0.625617 0.946650 0.343589 0.381559 -0.167683 -1.652701 1.941279 \n",
"365 -0.018941 -0.296919 0.643877 1.094566 0.281830 -1.407360 1.414953 \n",
"657 -0.973794 0.735844 0.302811 0.673145 0.283865 -0.848070 0.960780 \n",
"\n",
" X0 X1 \n",
"255 0.326860 -0.952641 \n",
"574 -0.240808 -1.072095 \n",
"571 1.410161 -0.684800 \n",
"914 0.977400 0.822046 \n",
"602 -1.360408 0.382768 \n",
".. ... ... \n",
"835 1.742445 0.346188 \n",
"622 0.278474 -0.135640 \n",
"425 1.941279 -1.652701 \n",
"365 1.414953 -1.407360 \n",
"657 0.960780 -0.848070 \n",
"\n",
"[1000 rows x 9 columns], 'y': 255 13.881950\n",
"574 5.633909\n",
"571 16.753939\n",
"914 12.801840\n",
"602 1.254728\n",
" ... \n",
"835 20.490073\n",
"622 8.845133\n",
"425 13.323890\n",
"365 15.195618\n",
"657 12.117853\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 255 True\n",
"574 True\n",
"571 True\n",
"914 True\n",
"602 True\n",
" ... \n",
"835 True\n",
"622 True\n",
"425 True\n",
"365 True\n",
"657 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Treatment): 0.1717\n",
"INFO:causalml: Gini (Control): 0.7444\n",
"INFO:causalml: Gini (Treatment): 0.9889\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0033\n",
"INFO:causalml: RMSE (Treatment): 0.9767\n",
"INFO:causalml: sMAPE (Control): 0.4587\n",
"INFO:causalml: sMAPE (Treatment): 0.1761\n",
"INFO:causalml: Gini (Control): 0.7124\n",
"INFO:causalml: Gini (Treatment): 0.9873\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9830\n",
"INFO:causalml: RMSE (Treatment): 1.0151\n",
"INFO:causalml: sMAPE (Control): 0.4974\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"57 -0.241496 0.341353 -0.879670 0.653348 0.562012 -0.874722 2.321090 \n",
"946 -0.039226 1.070254 0.817258 1.531369 -0.729411 -1.606529 1.682653 \n",
"779 -1.350659 0.815369 0.127656 0.103901 0.278969 -0.815212 0.066001 \n",
"896 -2.432024 -0.896851 1.092218 0.425996 -1.353030 -0.393840 -1.777303 \n",
"403 -1.548241 2.346455 0.703124 0.469986 -2.174662 -0.597804 -1.659799 \n",
".. ... ... ... ... ... ... ... \n",
"500 -0.764798 -0.741966 0.661109 1.471012 1.249719 -0.250612 1.784505 \n",
"353 -0.129342 0.937341 -0.341557 0.065221 -1.231137 0.286639 2.308218 \n",
"100 -1.378639 1.891778 -0.944875 2.350588 -0.498995 -1.187752 0.950568 \n",
"384 -0.985811 0.428972 -1.376992 -0.276737 -0.658336 0.236872 0.755765 \n",
"427 0.428906 0.044837 0.574607 1.415539 -1.691916 -0.202051 1.901581 \n",
"\n",
" X0 X1 \n",
"57 2.321090 -0.874722 \n",
"946 1.682653 -1.606529 \n",
"779 0.066001 -0.815212 \n",
"896 -1.777303 -0.393840 \n",
"403 -1.659799 -0.597804 \n",
".. ... ... \n",
"500 1.784505 -0.250612 \n",
"353 2.308218 0.286639 \n",
"100 0.950568 -1.187752 \n",
"384 0.755765 0.236872 \n",
"427 1.901581 -0.202051 \n",
"\n",
"[1000 rows x 9 columns], 'y': 57 18.788397\n",
"946 16.526801\n",
"779 7.001850\n",
"896 -7.004058\n",
"403 -3.265557\n",
" ... \n",
"500 17.859054\n",
"353 16.031327\n",
"100 11.113321\n",
"384 -5.168611\n",
"427 15.122153\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 57 True\n",
"946 True\n",
"779 True\n",
"896 True\n",
"403 True\n",
" ... \n",
"500 True\n",
"353 True\n",
"100 True\n",
"384 False\n",
"427 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"882 -2.069892 0.626586 -1.832899 -0.427297 1.053283 -0.782349 -0.035057 \n",
"365 -0.010483 -0.356730 0.588927 1.041733 0.428563 -1.548626 1.165125 \n",
"645 0.895264 1.665214 -0.241590 -0.355268 -2.385213 -1.247615 0.714368 \n",
"183 -1.739849 0.790751 0.101503 -0.955607 0.571522 0.000875 0.226301 \n",
"160 -1.739012 2.249436 0.471353 1.260166 0.195565 -0.286865 1.957172 \n",
".. ... ... ... ... ... ... ... \n",
"559 -1.348577 -0.508567 0.135080 0.760634 -0.578021 -1.684832 0.011024 \n",
"536 -1.320728 0.180165 -0.598624 -0.357499 -0.200479 1.283381 -0.942567 \n",
"387 -1.085304 1.898855 -0.049487 -0.493745 1.112310 -1.294861 2.878083 \n",
"652 -1.434432 -0.515402 -2.014047 0.739681 0.447348 0.527605 -0.378463 \n",
"466 0.169418 1.495641 -0.416136 1.897580 -0.623185 0.534709 1.641410 \n",
"\n",
" X0 X1 \n",
"882 -0.035057 -0.782349 \n",
"365 1.165125 -1.548626 \n",
"645 0.714368 -1.247615 \n",
"183 0.226301 0.000875 \n",
"160 1.957172 -0.286865 \n",
".. ... ... \n",
"559 0.011024 -1.684832 \n",
"536 -0.942567 1.283381 \n",
"387 2.878083 -1.294861 \n",
"652 -0.378463 0.527605 \n",
"466 1.641410 0.534709 \n",
"\n",
"[1000 rows x 9 columns], 'y': 882 -4.513230\n",
"365 15.195618\n",
"645 8.189153\n",
"183 5.804148\n",
"160 15.240536\n",
" ... \n",
"559 2.992068\n",
"536 3.123954\n",
"387 19.096949\n",
"652 -3.763410\n",
"466 18.638368\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 882 False\n",
"365 True\n",
"645 True\n",
"183 True\n",
"160 True\n",
" ... \n",
"559 True\n",
"536 True\n",
"387 True\n",
"652 False\n",
"466 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Treatment): 0.1793\n",
"INFO:causalml: Gini (Control): 0.7089\n",
"INFO:causalml: Gini (Treatment): 0.9869\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1606\n",
"INFO:causalml: RMSE (Treatment): 0.9664\n",
"INFO:causalml: sMAPE (Control): 0.5522\n",
"INFO:causalml: sMAPE (Treatment): 0.1688\n",
"INFO:causalml: Gini (Control): 0.6994\n",
"INFO:causalml: Gini (Treatment): 0.9879\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"62 -0.489888 0.421805 -3.885209 -1.142352 -0.661527 -1.367046 0.190653 \n",
"701 -0.397797 -1.049307 -0.657659 0.561405 -0.665239 0.960965 1.657074 \n",
"646 -0.505355 1.022138 0.422064 1.678294 -1.630333 -0.457794 0.460388 \n",
"698 -1.127903 1.739689 0.090811 1.239873 -1.455543 0.148345 -0.075825 \n",
"320 -1.348275 1.640355 0.014880 -0.332567 -0.476067 0.113882 1.460478 \n",
".. ... ... ... ... ... ... ... \n",
"671 -1.830457 1.927874 0.108815 0.686607 -1.445390 -0.412422 -0.739515 \n",
"227 -0.617478 2.117264 -0.796843 1.345096 0.100885 -1.472998 0.194239 \n",
"832 -0.635179 1.771168 -0.200905 -0.479498 -0.068345 -1.282431 0.163844 \n",
"170 -1.176175 2.724439 -1.543883 0.238433 -0.518789 -0.637966 -0.519854 \n",
"715 -0.437745 0.651706 -0.292567 -1.485070 -1.177736 -0.601624 -0.962816 \n",
"\n",
" X0 X1 \n",
"62 0.190653 -1.367046 \n",
"701 1.657074 0.960965 \n",
"646 0.460388 -0.457794 \n",
"698 -0.075825 0.148345 \n",
"320 1.460478 0.113882 \n",
".. ... ... \n",
"671 -0.739515 -0.412422 \n",
"227 0.194239 -1.472998 \n",
"832 0.163844 -1.282431 \n",
"170 -0.519854 -0.637966 \n",
"715 -0.962816 -0.601624 \n",
"\n",
"[1000 rows x 9 columns], 'y': 62 -5.979631\n",
"701 13.534369\n",
"646 9.399353\n",
"698 7.381116\n",
"320 11.942735\n",
" ... \n",
"671 -6.150284\n",
"227 9.555874\n",
"832 7.911675\n",
"170 -2.441764\n",
"715 0.458874\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 62 False\n",
"701 True\n",
"646 True\n",
"698 True\n",
"320 True\n",
" ... \n",
"671 False\n",
"227 True\n",
"832 True\n",
"170 False\n",
"715 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"173 -1.890919 0.303230 -1.853986 1.086205 -1.063154 -1.480039 0.557782 \n",
"452 -0.908609 1.446287 0.602898 1.273491 -0.728457 -2.722508 0.638021 \n",
"362 -2.772817 1.633125 0.477258 -1.222814 -1.376002 -1.625118 0.168268 \n",
"856 -2.213905 1.100170 -1.873703 -0.831352 -2.196694 -2.148791 -0.410641 \n",
"778 -2.232317 -0.354925 0.024290 0.391985 0.347478 -1.752844 0.687425 \n",
".. ... ... ... ... ... ... ... \n",
"648 0.687883 -0.708345 0.617329 -1.582533 -2.457736 -1.103158 0.881376 \n",
"931 -0.749425 0.972833 -0.048579 -0.511019 -0.159517 -1.135516 0.958728 \n",
"202 -1.547532 1.893259 1.899565 1.088557 -0.511950 -1.168168 -1.399601 \n",
"314 -0.929180 -0.435418 -2.500459 0.362450 0.110227 -2.574999 1.657417 \n",
"885 -2.073608 -0.503616 -1.645188 0.427103 -1.614990 0.502726 0.944974 \n",
"\n",
" X0 X1 \n",
"173 0.557782 -1.480039 \n",
"452 0.638021 -2.722508 \n",
"362 0.168268 -1.625118 \n",
"856 -0.410641 -2.148791 \n",
"778 0.687425 -1.752844 \n",
".. ... ... \n",
"648 0.881376 -1.103158 \n",
"931 0.958728 -1.135516 \n",
"202 -1.399601 -1.168168 \n",
"314 1.657417 -2.574999 \n",
"885 0.944974 0.502726 \n",
"\n",
"[1000 rows x 9 columns], 'y': 173 -7.857862\n",
"452 7.577603\n",
"362 -3.054046\n",
"856 -13.300039\n",
"778 6.019405\n",
" ... \n",
"648 4.759991\n",
"931 10.080089\n",
"202 2.297590\n",
"314 -3.811119\n",
"885 -11.199742\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 173 False\n",
"452 True\n",
"362 True\n",
"856 False\n",
"778 True\n",
" ... \n",
"648 True\n",
"931 True\n",
"202 True\n",
"314 False\n",
"885 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8703\n",
"INFO:causalml: RMSE (Treatment): 0.9484\n",
"INFO:causalml: sMAPE (Control): 0.4835\n",
"INFO:causalml: sMAPE (Treatment): 0.1689\n",
"INFO:causalml: Gini (Control): 0.7614\n",
"INFO:causalml: Gini (Treatment): 0.9884\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0918\n",
"INFO:causalml: RMSE (Treatment): 1.0446\n",
"INFO:causalml: sMAPE (Control): 0.4937\n",
"INFO:causalml: sMAPE (Treatment): 0.1971\n",
"INFO:causalml: Gini (Control): 0.7099\n",
"INFO:causalml: Gini (Treatment): 0.9853\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9127\n",
"INFO:causalml: RMSE (Treatment): 0.8881\n",
"INFO:causalml: sMAPE (Control): 0.5185\n",
"INFO:causalml: sMAPE (Treatment): 0.1651\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"229 -1.693449 0.975769 0.294856 1.677213 0.570499 0.236995 3.001468 \n",
"32 -1.099286 0.898796 0.284389 -0.614014 -1.150040 0.589624 0.196437 \n",
"407 -1.199841 0.583590 -0.114395 -0.810430 0.316471 1.190869 0.694040 \n",
"883 -1.139762 0.160852 -0.071674 -0.104197 -1.540943 0.839832 0.628072 \n",
"155 0.186297 2.211595 0.272060 1.710993 -0.343155 0.940461 1.044007 \n",
".. ... ... ... ... ... ... ... \n",
"884 -0.206463 2.515624 -0.597811 0.358788 -1.639772 -1.142756 0.578135 \n",
"107 -1.391807 0.824917 -0.114520 0.917218 -0.346789 -0.623044 1.090865 \n",
"345 -1.194521 2.556273 0.304178 -0.438781 -0.127703 -0.705603 1.153798 \n",
"174 -1.389069 2.427555 -1.962768 0.380924 -2.720164 -0.777716 -0.018113 \n",
"132 -4.057538 1.581352 -1.081639 1.262583 -2.059606 0.227712 0.373265 \n",
"\n",
" X0 X1 \n",
"229 3.001468 0.236995 \n",
"32 0.196437 0.589624 \n",
"407 0.694040 1.190869 \n",
"883 0.628072 0.839832 \n",
"155 1.044007 0.940461 \n",
".. ... ... \n",
"884 0.578135 -1.142756 \n",
"107 1.090865 -0.623044 \n",
"345 1.153798 -0.705603 \n",
"174 -0.018113 -0.777716 \n",
"132 0.373265 0.227712 \n",
"\n",
"[1000 rows x 9 columns], 'y': 229 21.267379\n",
"32 -5.941016\n",
"407 10.716455\n",
"883 5.901248\n",
"155 19.147114\n",
" ... \n",
"884 9.392570\n",
"107 -2.840767\n",
"345 12.057991\n",
"174 1.619585\n",
"132 -3.629107\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 229 True\n",
"32 False\n",
"407 True\n",
"883 True\n",
"155 True\n",
" ... \n",
"884 True\n",
"107 False\n",
"345 True\n",
"174 True\n",
"132 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"486 -0.060378 -0.386603 -0.624311 0.427205 0.865676 -1.184077 0.559674 \n",
"584 -0.754149 0.524564 0.259186 0.327950 0.764248 1.517739 -0.343597 \n",
"393 -1.303196 0.605609 0.168569 1.115444 -0.924550 -0.102834 0.081622 \n",
"715 -0.612551 0.682669 -0.199036 -1.235912 -1.123797 -0.718232 -0.907246 \n",
"287 -2.030588 -0.724655 -2.152445 1.455423 -1.699869 0.777392 0.490297 \n",
".. ... ... ... ... ... ... ... \n",
"138 0.611603 0.658839 -0.438636 1.626230 -1.021618 0.334653 -0.099974 \n",
"70 -0.565836 1.834783 -2.122074 -1.142553 0.905590 -0.660720 1.852484 \n",
"723 0.034264 1.444662 0.785691 0.755711 -0.643581 -0.377432 0.660484 \n",
"538 -0.823910 2.479396 0.152882 1.342818 1.880938 -1.872310 0.653014 \n",
"697 -0.914739 0.515895 -1.237094 0.455332 -0.697591 -2.569362 -0.049337 \n",
"\n",
" X0 X1 \n",
"486 0.559674 -1.184077 \n",
"584 -0.343597 1.517739 \n",
"393 0.081622 -0.102834 \n",
"715 -0.907246 -0.718232 \n",
"287 0.490297 0.777392 \n",
".. ... ... \n",
"138 -0.099974 0.334653 \n",
"70 1.852484 -0.660720 \n",
"723 0.660484 -0.377432 \n",
"538 0.653014 -1.872310 \n",
"697 -0.049337 -2.569362 \n",
"\n",
"[1000 rows x 9 columns], 'y': 486 12.711964\n",
"584 11.093724\n",
"393 -4.149152\n",
"715 0.458874\n",
"287 -11.017227\n",
" ... \n",
"138 11.274922\n",
"70 15.668385\n",
"723 12.592456\n",
"538 16.761939\n",
"697 3.459264\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 486 True\n",
"584 True\n",
"393 False\n",
"715 True\n",
"287 False\n",
" ... \n",
"138 True\n",
"70 True\n",
"723 True\n",
"538 True\n",
"697 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Control): 0.6943\n",
"INFO:causalml: Gini (Treatment): 0.9893\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9119\n",
"INFO:causalml: RMSE (Treatment): 1.1321\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"498 0.925632 1.248556 0.266050 0.624337 0.139784 0.054055 0.611980 \n",
"343 -1.275755 1.853760 0.893049 -1.703371 -0.923133 -1.113825 0.470165 \n",
"406 -1.723518 0.869494 -0.410107 -0.411069 0.242774 1.029431 0.633780 \n",
"347 -0.783681 -0.425386 -1.016921 1.284220 -1.134495 -0.170870 -0.188672 \n",
"107 -1.302936 0.849902 -0.159648 0.948353 -0.246683 -0.672936 0.827165 \n",
".. ... ... ... ... ... ... ... \n",
"650 -1.089455 0.154798 -2.850727 1.223669 0.311584 -1.655243 0.105204 \n",
"109 -1.315574 0.194043 -0.454507 0.856102 1.277240 -0.215829 2.329545 \n",
"329 -0.763798 0.152340 -0.132608 0.221868 -1.791237 0.079281 2.224855 \n",
"960 0.161576 -0.287115 0.066753 -0.917243 -2.944296 -0.165028 1.221761 \n",
"106 -1.481454 1.547463 0.125611 -0.352701 -2.068552 -1.862149 0.736369 \n",
"\n",
" X0 X1 \n",
"498 0.611980 0.054055 \n",
"343 0.470165 -1.113825 \n",
"406 0.633780 1.029431 \n",
"347 -0.188672 -0.170870 \n",
"107 0.827165 -0.672936 \n",
".. ... ... \n",
"650 0.105204 -1.655243 \n",
"109 2.329545 -0.215829 \n",
"329 2.224855 0.079281 \n",
"960 1.221761 -0.165028 \n",
"106 0.736369 -1.862149 \n",
"\n",
"[1000 rows x 9 columns], 'y': 498 17.581110\n",
"343 4.731928\n",
"406 9.585905\n",
"347 4.085427\n",
"107 -2.840767\n",
" ... \n",
"650 -2.393578\n",
"109 18.593730\n",
"329 10.777317\n",
"960 4.643805\n",
"106 -8.631314\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 498 True\n",
"343 True\n",
"406 True\n",
"347 True\n",
"107 False\n",
" ... \n",
"650 False\n",
"109 True\n",
"329 True\n",
"960 True\n",
"106 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Control): 0.5824\n",
"INFO:causalml: sMAPE (Treatment): 0.1933\n",
"INFO:causalml: Gini (Control): 0.7839\n",
"INFO:causalml: Gini (Treatment): 0.9837\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0786\n",
"INFO:causalml: RMSE (Treatment): 0.9618\n",
"INFO:causalml: sMAPE (Control): 0.5333\n",
"INFO:causalml: sMAPE (Treatment): 0.1850\n",
"INFO:causalml: Gini (Control): 0.7419\n",
"INFO:causalml: Gini (Treatment): 0.9893\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"181 1.125728 0.140116 0.069322 0.831179 -0.385813 -0.950129 0.420018 \n",
"422 -1.770041 0.841889 -0.224366 -0.392414 2.191028 0.024989 0.319091 \n",
"659 -0.723129 2.909802 -0.962345 2.917851 -1.736066 -0.935685 -0.665218 \n",
"51 -0.289412 0.743801 1.214467 -0.131556 -0.254289 -0.561595 -0.126134 \n",
"684 -2.040331 2.247077 0.239600 0.453179 1.300511 -0.708659 0.690296 \n",
".. ... ... ... ... ... ... ... \n",
"874 0.536244 2.342032 0.843026 2.121612 -1.791246 -0.603460 1.230203 \n",
"146 -0.789941 -0.290262 -1.449763 0.313378 0.341513 -0.942170 -1.177943 \n",
"162 -1.414215 0.924055 -0.993010 0.433887 1.924170 -2.278847 2.402717 \n",
"972 -0.083595 0.625131 -0.987192 1.608500 1.194546 -1.260879 -0.750448 \n",
"426 -0.539558 1.140058 -0.611717 -0.200436 0.854985 -0.856433 -0.357662 \n",
"\n",
" X0 X1 \n",
"181 0.420018 -0.950129 \n",
"422 0.319091 0.024989 \n",
"659 -0.665218 -0.935685 \n",
"51 -0.126134 -0.561595 \n",
"684 0.690296 -0.708659 \n",
".. ... ... \n",
"874 1.230203 -0.603460 \n",
"146 -1.177943 -0.942170 \n",
"162 2.402717 -2.278847 \n",
"972 -0.750448 -1.260879 \n",
"426 -0.357662 -0.856433 \n",
"\n",
"[1000 rows x 9 columns], 'y': 181 13.569762\n",
"422 11.141529\n",
"659 4.772285\n",
"51 8.374283\n",
"684 11.451222\n",
" ... \n",
"874 16.052226\n",
"146 3.922772\n",
"162 17.934588\n",
"972 10.333966\n",
"426 8.947595\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 181 True\n",
"422 True\n",
"659 True\n",
"51 True\n",
"684 True\n",
" ... \n",
"874 True\n",
"146 True\n",
"162 True\n",
"972 True\n",
"426 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9047\n",
"INFO:causalml: RMSE (Treatment): 0.9695\n",
"INFO:causalml: sMAPE (Control): 0.4927\n",
"INFO:causalml: sMAPE (Treatment): 0.1624\n",
"INFO:causalml: Gini (Control): 0.7019\n",
"INFO:causalml: Gini (Treatment): 0.9864\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"716 -1.615944 1.592631 -1.577118 -0.974178 1.417172 -0.195000 0.312512 \n",
"774 -0.886076 0.195586 0.455056 -0.785569 0.251063 -1.413741 0.262704 \n",
"272 -3.301648 0.553178 0.159603 2.046934 -0.388566 -1.527966 1.482174 \n",
"443 -2.529796 1.084026 -0.819423 0.035893 0.753495 0.483814 -0.069741 \n",
"854 -2.523311 -0.010257 -0.769967 -0.177994 0.185291 -0.178115 0.296577 \n",
".. ... ... ... ... ... ... ... \n",
"614 -2.386008 1.131412 -0.505893 0.004830 0.101923 -0.350414 -0.390763 \n",
"826 -1.751728 1.126503 -0.508416 0.008477 -0.550763 -2.243036 0.627975 \n",
"279 -2.061036 -0.503615 -0.241566 -0.116680 0.053960 0.623388 1.384687 \n",
"24 -0.551295 0.322400 -1.152389 1.649997 -2.529414 -1.033174 0.650334 \n",
"312 -0.728990 -0.366328 0.830242 0.664962 -0.175745 -0.044821 0.803275 \n",
"\n",
" X0 X1 \n",
"716 0.312512 -0.195000 \n",
"774 0.262704 -1.413741 \n",
"272 1.482174 -1.527966 \n",
"443 -0.069741 0.483814 \n",
"854 0.296577 -0.178115 \n",
".. ... ... \n",
"614 -0.390763 -0.350414 \n",
"826 0.627975 -2.243036 \n",
"279 1.384687 0.623388 \n",
"24 0.650334 -1.033174 \n",
"312 0.803275 -0.044821 \n",
"\n",
"[1000 rows x 9 columns], 'y': 716 9.358799\n",
"774 6.080336\n",
"272 -7.247960\n",
"443 6.116446\n",
"854 -8.250436\n",
" ... \n",
"614 -5.992837\n",
"826 3.753175\n",
"279 -6.155145\n",
"24 -7.244556\n",
"312 10.376441\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 716 True\n",
"774 True\n",
"272 False\n",
"443 True\n",
"854 False\n",
" ... \n",
"614 False\n",
"826 True\n",
"279 False\n",
"24 False\n",
"312 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"148 -0.427906 -0.348349 -1.414734 -0.840811 -1.436732 -1.544825 1.050592 \n",
"683 -0.565221 -1.461757 -1.292868 0.617291 0.720147 0.269804 0.479413 \n",
"782 -0.683582 2.220805 -0.634412 1.335696 -1.692552 -0.549927 2.153804 \n",
"323 -1.189912 1.017885 -1.046517 -0.238839 -0.588009 0.098981 0.492976 \n",
"229 -1.566769 1.166166 0.379113 1.564834 0.393456 0.321811 3.489682 \n",
".. ... ... ... ... ... ... ... \n",
"303 -1.802541 1.155115 0.033706 1.224001 0.978380 -0.626800 0.580053 \n",
"356 -1.116602 0.982596 -0.581222 -0.520757 -0.778744 0.510570 1.240015 \n",
"180 -0.755650 0.531365 -1.277980 -1.074982 1.894625 -0.207842 0.434392 \n",
"117 -2.281886 0.960677 -2.020716 -1.229467 -1.118221 -0.754915 0.968480 \n",
"216 -1.445130 1.697550 -2.520408 -0.942383 -0.658474 -1.404127 0.107552 \n",
"\n",
" X0 X1 \n",
"148 1.050592 -1.544825 \n",
"683 0.479413 0.269804 \n",
"782 2.153804 -0.549927 \n",
"323 0.492976 0.098981 \n",
"229 3.489682 0.321811 \n",
".. ... ... \n",
"303 0.580053 -0.626800 \n",
"356 1.240015 0.510570 \n",
"180 0.434392 -0.207842 \n",
"117 0.968480 -0.754915 \n",
"216 0.107552 -1.404127 \n",
"\n",
"[1000 rows x 9 columns], 'y': 148 4.946877\n",
"683 -0.955793\n",
"782 14.584633\n",
"323 6.435152\n",
"229 21.267379\n",
" ... \n",
"303 11.687300\n",
"356 -4.713297\n",
"180 12.418586\n",
"117 -11.180244\n",
"216 2.376140\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 148 True\n",
"683 False\n",
"782 True\n",
"323 True\n",
"229 True\n",
" ... \n",
"303 True\n",
"356 False\n",
"180 True\n",
"117 False\n",
"216 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0486\n",
"INFO:causalml: RMSE (Treatment): 0.9171\n",
"INFO:causalml: sMAPE (Control): 0.5529\n",
"INFO:causalml: sMAPE (Treatment): 0.1573\n",
"INFO:causalml: Gini (Control): 0.7084\n",
"INFO:causalml: Gini (Treatment): 0.9884\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0082\n",
"INFO:causalml: RMSE (Treatment): 0.9557\n",
"INFO:causalml: sMAPE (Control): 0.5230\n",
"INFO:causalml: sMAPE (Treatment): 0.1729\n",
"INFO:causalml: Gini (Control): 0.7377\n",
"INFO:causalml: Gini (Treatment): 0.9884\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"168 0.450300 1.390503 0.155373 -1.101036 -0.952799 -0.887511 1.430561 \n",
"176 -0.856629 1.249011 0.865558 0.345553 -2.692244 -0.310500 -0.444710 \n",
"712 -0.384708 1.043094 -0.342221 1.242046 -0.192181 -0.562779 -1.078074 \n",
"540 -1.489472 1.850874 -0.563384 0.019167 2.261024 0.812529 1.334890 \n",
"243 -3.249216 -0.470659 0.290909 1.619751 -1.136002 -0.305133 0.820941 \n",
".. ... ... ... ... ... ... ... \n",
"78 -1.204209 0.126061 0.575761 1.962184 -2.467364 -0.763519 -0.076058 \n",
"578 -0.204916 -0.336268 -0.512652 -0.557115 -0.290550 -0.190070 0.805354 \n",
"580 0.010203 1.941732 -0.233593 0.219375 0.073492 -2.284840 -0.308323 \n",
"967 -1.474196 1.682763 -1.012478 1.377707 -0.535955 -3.259822 1.413777 \n",
"878 -1.217552 2.664742 -2.231582 1.034746 -0.096639 -1.053233 0.765971 \n",
"\n",
" X0 X1 \n",
"168 1.430561 -0.887511 \n",
"176 -0.444710 -0.310500 \n",
"712 -1.078074 -0.562779 \n",
"540 1.334890 0.812529 \n",
"243 0.820941 -0.305133 \n",
".. ... ... \n",
"78 -0.076058 -0.763519 \n",
"578 0.805354 -0.190070 \n",
"580 -0.308323 -2.284840 \n",
"967 1.413777 -3.259822 \n",
"878 0.765971 -1.053233 \n",
"\n",
"[1000 rows x 9 columns], 'y': 168 13.539036\n",
"176 0.810587\n",
"712 6.780320\n",
"540 19.517995\n",
"243 -10.483545\n",
" ... \n",
"78 2.567970\n",
"578 -2.946371\n",
"580 8.433110\n",
"967 -2.657495\n",
"878 11.079847\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 168 True\n",
"176 True\n",
"712 True\n",
"540 True\n",
"243 False\n",
" ... \n",
"78 True\n",
"578 False\n",
"580 True\n",
"967 False\n",
"878 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9013\n",
"INFO:causalml: RMSE (Treatment): 0.9553\n",
"INFO:causalml: sMAPE (Control): 0.5311\n",
"INFO:causalml: sMAPE (Treatment): 0.1842\n",
"INFO:causalml: Gini (Control): 0.7100\n",
"INFO:causalml: Gini (Treatment): 0.9884\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"787 0.073705 2.848665 -1.943361 -1.359323 -0.878067 -0.436985 -0.313540 \n",
"906 -1.575434 0.723096 0.059694 2.122850 0.024433 0.756446 0.800562 \n",
"782 -0.746773 2.414147 -0.784415 1.453598 -1.809795 -0.543201 2.299416 \n",
"357 -0.378320 -0.884596 -0.669495 -0.885519 -0.360368 0.592724 -0.790022 \n",
"242 -1.792347 1.490790 -2.219958 0.454298 1.183930 -1.147240 -1.586327 \n",
".. ... ... ... ... ... ... ... \n",
"5 0.099175 -0.058453 -0.246124 -1.474818 -0.040728 -1.233210 -0.794351 \n",
"557 0.204105 -0.449736 0.150027 1.430447 -1.525944 -1.113027 -0.164975 \n",
"545 -1.038618 2.006304 0.732717 1.104913 0.596121 0.500374 0.898176 \n",
"833 0.101179 0.209330 0.769188 1.227717 -0.727485 -1.215284 1.049029 \n",
"691 -0.929421 1.731717 0.245810 1.374453 -1.257481 -2.478499 1.097686 \n",
"\n",
" X0 X1 \n",
"787 -0.313540 -0.436985 \n",
"906 0.800562 0.756446 \n",
"782 2.299416 -0.543201 \n",
"357 -0.790022 0.592724 \n",
"242 -1.586327 -1.147240 \n",
".. ... ... \n",
"5 -0.794351 -1.233210 \n",
"557 -0.164975 -1.113027 \n",
"545 0.898176 0.500374 \n",
"833 1.049029 -1.215284 \n",
"691 1.097686 -2.478499 \n",
"\n",
"[1000 rows x 9 columns], 'y': 787 7.397373\n",
"906 11.554534\n",
"782 14.584633\n",
"357 4.629609\n",
"242 1.894728\n",
" ... \n",
"5 4.741680\n",
"557 5.886610\n",
"545 15.065277\n",
"833 13.445887\n",
"691 9.514980\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 787 True\n",
"906 True\n",
"782 True\n",
"357 True\n",
"242 True\n",
" ... \n",
"5 True\n",
"557 True\n",
"545 True\n",
"833 True\n",
"691 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9654\n",
"INFO:causalml: RMSE (Treatment): 0.9024\n",
"INFO:causalml: sMAPE (Control): 0.5116\n",
"INFO:causalml: sMAPE (Treatment): 0.1687\n",
"INFO:causalml: Gini (Control): 0.6677\n",
"INFO:causalml: Gini (Treatment): 0.9897\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"896 -2.465205 -0.999605 1.043228 0.318869 -1.465928 -0.503822 -1.540073 \n",
"924 -1.012879 1.098058 -0.604224 -1.358602 -0.086915 1.405343 1.440895 \n",
"512 -1.440033 1.671872 1.455479 2.996854 -1.379065 -1.149984 0.578248 \n",
"426 -0.315671 1.199681 -0.931325 -0.273054 0.861289 -0.838370 -0.409759 \n",
"896 -2.555420 -0.866699 1.067359 0.461713 -1.476452 -0.242811 -1.659653 \n",
".. ... ... ... ... ... ... ... \n",
"507 -1.437455 -0.337114 -1.649122 0.872149 -1.304679 -2.133027 0.785621 \n",
"204 -1.476774 1.668028 -1.614169 0.703887 1.188127 0.282770 2.283149 \n",
"170 -1.251671 2.617097 -1.391555 0.287190 -0.554072 -0.494777 -0.731169 \n",
"406 -1.799370 0.523082 -0.214739 -0.356074 0.020431 1.217244 0.629496 \n",
"96 1.225402 0.765896 -0.047135 1.089486 -1.500370 -1.094204 0.803605 \n",
"\n",
" X0 X1 \n",
"896 -1.540073 -0.503822 \n",
"924 1.440895 1.405343 \n",
"512 0.578248 -1.149984 \n",
"426 -0.409759 -0.838370 \n",
"896 -1.659653 -0.242811 \n",
".. ... ... \n",
"507 0.785621 -2.133027 \n",
"204 2.283149 0.282770 \n",
"170 -0.731169 -0.494777 \n",
"406 0.629496 1.217244 \n",
"96 0.803605 -1.094204 \n",
"\n",
"[1000 rows x 9 columns], 'y': 896 -7.004058\n",
"924 12.759503\n",
"512 8.367137\n",
"426 8.947595\n",
"896 -7.004058\n",
" ... \n",
"507 -8.258449\n",
"204 18.498160\n",
"170 -2.441764\n",
"406 9.585905\n",
"96 14.140134\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 896 True\n",
"924 True\n",
"512 True\n",
"426 True\n",
"896 True\n",
" ... \n",
"507 False\n",
"204 True\n",
"170 False\n",
"406 True\n",
"96 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9740\n",
"INFO:causalml: RMSE (Treatment): 0.9701\n",
"INFO:causalml: sMAPE (Control): 0.5245\n",
"INFO:causalml: sMAPE (Treatment): 0.1888\n",
"INFO:causalml: Gini (Control): 0.7661\n",
"INFO:causalml: Gini (Treatment): 0.9887\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"531 -1.254262 -0.167187 -1.118310 0.614412 1.198764 -0.896836 -0.290468 \n",
"740 -1.758760 0.145873 -0.995149 1.417127 0.854187 -0.493563 1.956534 \n",
"484 -2.266697 0.849631 -1.660032 -0.689205 0.906942 -1.756428 -1.829463 \n",
"978 -0.867775 2.183924 0.075721 0.981680 0.330304 -0.403923 2.232607 \n",
"381 0.283811 0.106543 -0.055161 -1.052739 -1.024427 -0.208798 -1.355256 \n",
".. ... ... ... ... ... ... ... \n",
"846 -1.635453 -0.271491 0.831266 0.243038 0.620326 0.957903 1.160719 \n",
"314 -0.826652 -0.553605 -2.421504 0.298919 0.060331 -2.639562 1.210031 \n",
"331 -1.054795 1.357207 -1.162656 0.466176 0.407668 -1.780166 0.733555 \n",
"532 -0.925465 1.279058 -0.889965 -0.707787 -0.241389 -1.093723 0.034877 \n",
"147 -2.132299 2.078401 -2.857642 -0.986338 0.869850 -0.541032 1.487452 \n",
"\n",
" X0 X1 \n",
"531 -0.290468 -0.896836 \n",
"740 1.956534 -0.493563 \n",
"484 -1.829463 -1.756428 \n",
"978 2.232607 -0.403923 \n",
"381 -1.355256 -0.208798 \n",
".. ... ... \n",
"846 1.160719 0.957903 \n",
"314 1.210031 -2.639562 \n",
"331 0.733555 -1.780166 \n",
"532 0.034877 -1.093723 \n",
"147 1.487452 -0.541032 \n",
"\n",
"[1000 rows x 9 columns], 'y': 531 7.735845\n",
"740 15.715393\n",
"484 -3.918315\n",
"978 19.702763\n",
"381 2.059536\n",
" ... \n",
"846 12.705539\n",
"314 -3.811119\n",
"331 10.231377\n",
"532 5.559274\n",
"147 10.091404\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 531 True\n",
"740 True\n",
"484 True\n",
"978 True\n",
"381 True\n",
" ... \n",
"846 True\n",
"314 False\n",
"331 True\n",
"532 True\n",
"147 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.2477\n",
"INFO:causalml: RMSE (Treatment): 1.0436\n",
"INFO:causalml: sMAPE (Control): 0.5956\n",
"INFO:causalml: sMAPE (Treatment): 0.1984\n",
"INFO:causalml: Gini (Control): 0.6722\n",
"INFO:causalml: Gini (Treatment): 0.9865\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"203 -1.114096 0.744064 -0.690975 0.599358 -0.792642 -1.637673 -0.397490 \n",
"257 -2.323848 3.016880 -1.700118 1.013322 -0.844318 0.618392 -0.839909 \n",
"538 -1.038158 2.444865 0.094385 1.527320 1.919538 -1.951172 0.524453 \n",
"990 -0.000793 0.988923 -0.186809 0.210013 -1.229710 0.232132 -0.020338 \n",
"731 -0.803858 1.991449 -1.228869 0.189832 -0.297466 -0.369540 1.348036 \n",
".. ... ... ... ... ... ... ... \n",
"315 -0.833272 0.226528 1.212836 1.772068 0.227297 -0.380696 -0.006080 \n",
"640 -1.671057 1.467272 -0.375628 1.326509 -1.537542 -0.711127 0.021974 \n",
"878 -0.962893 2.730247 -2.113489 0.862045 -0.149434 -1.135810 1.112920 \n",
"610 -0.524884 1.540807 -0.035025 -0.035810 -1.185091 -0.813191 0.907323 \n",
"385 0.165748 -0.396830 -0.189713 0.224653 -1.901237 -1.310045 -0.920344 \n",
"\n",
" X0 X1 \n",
"203 -0.397490 -1.637673 \n",
"257 -0.839909 0.618392 \n",
"538 0.524453 -1.951172 \n",
"990 -0.020338 0.232132 \n",
"731 1.348036 -0.369540 \n",
".. ... ... \n",
"315 -0.006080 -0.380696 \n",
"640 0.021974 -0.711127 \n",
"878 1.112920 -1.135810 \n",
"610 0.907323 -0.813191 \n",
"385 -0.920344 -1.310045 \n",
"\n",
"[1000 rows x 9 columns], 'y': 203 -4.155357\n",
"257 2.568588\n",
"538 16.761939\n",
"990 8.714440\n",
"731 13.912472\n",
" ... \n",
"315 11.545213\n",
"640 3.839554\n",
"878 11.079847\n",
"610 9.248446\n",
"385 -1.265529\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 203 False\n",
"257 True\n",
"538 True\n",
"990 True\n",
"731 True\n",
" ... \n",
"315 True\n",
"640 True\n",
"878 True\n",
"610 True\n",
"385 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9891\n",
"INFO:causalml: RMSE (Treatment): 0.9589\n",
"INFO:causalml: sMAPE (Control): 0.5070\n",
"INFO:causalml: sMAPE (Treatment): 0.1700\n",
"INFO:causalml: Gini (Control): 0.7410\n",
"INFO:causalml: Gini (Treatment): 0.9883\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"584 -0.774744 0.506187 0.455158 0.141210 0.990320 1.628572 -0.711597 \n",
"841 -0.724696 -0.374489 -1.103751 1.129970 0.018946 -1.663767 0.567341 \n",
"885 -2.180824 -0.176545 -1.411294 0.282502 -1.568631 0.486085 0.855786 \n",
"288 -2.020792 1.845664 0.351894 0.727023 -1.539883 0.047874 0.409374 \n",
"207 -1.188113 0.958327 1.154980 0.336073 1.815925 -2.040130 0.659767 \n",
".. ... ... ... ... ... ... ... \n",
"352 -0.535340 -0.482681 2.379011 0.602865 -0.376757 0.429827 -0.246536 \n",
"56 -0.256722 0.790392 -1.288484 -0.016408 -0.773910 -0.151304 0.797364 \n",
"260 -2.400965 -1.351992 -1.401867 0.351487 0.142640 -1.172623 -1.246456 \n",
"265 -0.624554 2.322689 0.829394 -1.257380 -0.054393 -0.181632 -0.055043 \n",
"434 -1.176635 1.246362 0.634036 -0.149590 -0.657726 -2.223801 -0.233063 \n",
"\n",
" X0 X1 \n",
"584 -0.711597 1.628572 \n",
"841 0.567341 -1.663767 \n",
"885 0.855786 0.486085 \n",
"288 0.409374 0.047874 \n",
"207 0.659767 -2.040130 \n",
".. ... ... \n",
"352 -0.246536 0.429827 \n",
"56 0.797364 -0.151304 \n",
"260 -1.246456 -1.172623 \n",
"265 -0.055043 -0.181632 \n",
"434 -0.233063 -2.223801 \n",
"\n",
"[1000 rows x 9 columns], 'y': 584 11.093724\n",
"841 9.168319\n",
"885 -11.199742\n",
"288 5.235864\n",
"207 13.068250\n",
" ... \n",
"352 8.970110\n",
"56 -1.924324\n",
"260 -8.117300\n",
"265 9.003069\n",
"434 1.849930\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 584 True\n",
"841 True\n",
"885 False\n",
"288 True\n",
"207 True\n",
" ... \n",
"352 True\n",
"56 False\n",
"260 False\n",
"265 True\n",
"434 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.8899\n",
"INFO:causalml: RMSE (Treatment): 0.8863\n",
"INFO:causalml: sMAPE (Control): 0.5197\n",
"INFO:causalml: sMAPE (Treatment): 0.1595\n",
"INFO:causalml: Gini (Control): 0.7407\n",
"INFO:causalml: Gini (Treatment): 0.9895\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"941 -0.797345 2.665011 0.065694 -0.869696 -0.413973 -0.687528 0.939537 \n",
"340 0.395646 2.063995 -0.674216 -0.736351 0.536852 -0.722070 1.665084 \n",
"303 -1.670465 1.107357 -0.022130 1.189194 0.769526 -0.515271 0.865204 \n",
"365 -0.221781 -0.295120 0.899051 0.889803 0.424290 -1.638042 1.211189 \n",
"473 -2.054937 3.017625 0.240892 0.295945 -1.512661 -1.911661 0.565662 \n",
".. ... ... ... ... ... ... ... \n",
"193 -0.518369 1.617026 -1.830014 -0.741120 -0.465983 -1.906328 0.215485 \n",
"21 -0.773907 1.110886 -0.544079 1.006438 -0.060069 -1.772730 -0.347305 \n",
"667 -2.226760 0.866911 0.115075 1.460086 -0.182291 0.234942 1.711669 \n",
"754 -1.061954 -0.022086 -0.504810 1.309105 0.785156 -0.609433 0.176682 \n",
"110 -1.835672 2.334799 -0.523204 1.565468 -0.553570 0.776834 1.039780 \n",
"\n",
" X0 X1 \n",
"941 0.939537 -0.687528 \n",
"340 1.665084 -0.722070 \n",
"303 0.865204 -0.515271 \n",
"365 1.211189 -1.638042 \n",
"473 0.565662 -1.911661 \n",
".. ... ... \n",
"193 0.215485 -1.906328 \n",
"21 -0.347305 -1.772730 \n",
"667 1.711669 0.234942 \n",
"754 0.176682 -0.609433 \n",
"110 1.039780 0.776834 \n",
"\n",
"[1000 rows x 9 columns], 'y': 941 9.630585\n",
"340 18.529106\n",
"303 11.687300\n",
"365 15.195618\n",
"473 2.964870\n",
" ... \n",
"193 5.787386\n",
"21 6.406231\n",
"667 11.985876\n",
"754 9.673575\n",
"110 11.934711\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 941 True\n",
"340 True\n",
"303 True\n",
"365 True\n",
"473 True\n",
" ... \n",
"193 True\n",
"21 True\n",
"667 True\n",
"754 True\n",
"110 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9757\n",
"INFO:causalml: RMSE (Treatment): 0.9511\n",
"INFO:causalml: sMAPE (Control): 0.5335\n",
"INFO:causalml: sMAPE (Treatment): 0.1826\n",
"INFO:causalml: Gini (Control): 0.7234\n",
"INFO:causalml: Gini (Treatment): 0.9876\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"439 -0.777678 0.791216 0.088420 -0.890809 -2.096758 -2.250654 -0.541533 \n",
"448 -1.966394 0.687084 -1.596052 0.310020 1.635333 -0.303399 -0.488773 \n",
"737 -0.437622 0.229345 -0.010522 0.433842 -1.561250 -1.020391 0.680363 \n",
"458 -1.283245 -0.238230 -0.732136 1.453553 -1.122988 0.246707 0.593596 \n",
"507 -1.573522 -0.282174 -1.553324 1.126635 -1.519532 -1.805382 1.103591 \n",
".. ... ... ... ... ... ... ... \n",
"689 -2.157866 1.144691 -1.437880 -2.358650 -1.930470 -2.442867 -0.507167 \n",
"817 0.535492 1.129122 -0.785106 0.005089 0.605181 -0.155125 1.160599 \n",
"979 -1.119402 -0.292695 0.065637 1.221340 1.164068 -0.975426 0.551811 \n",
"447 -0.452422 0.225148 -0.548859 -0.857193 -0.796498 -1.718787 0.004224 \n",
"318 -0.436870 -0.478021 -0.215719 -0.049107 1.442010 -0.572800 -0.385856 \n",
"\n",
" X0 X1 \n",
"439 -0.541533 -2.250654 \n",
"448 -0.488773 -0.303399 \n",
"737 0.680363 -1.020391 \n",
"458 0.593596 0.246707 \n",
"507 1.103591 -1.805382 \n",
".. ... ... \n",
"689 -0.507167 -2.442867 \n",
"817 1.160599 -0.155125 \n",
"979 0.551811 -0.975426 \n",
"447 0.004224 -1.718787 \n",
"318 -0.385856 -0.572800 \n",
"\n",
"[1000 rows x 9 columns], 'y': 439 -2.654670\n",
"448 -0.866495\n",
"737 6.679250\n",
"458 7.125670\n",
"507 -8.258449\n",
" ... \n",
"689 -13.994008\n",
"817 17.203287\n",
"979 10.393456\n",
"447 3.661764\n",
"318 10.068543\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 439 True\n",
"448 False\n",
"737 True\n",
"458 True\n",
"507 False\n",
" ... \n",
"689 False\n",
"817 True\n",
"979 True\n",
"447 True\n",
"318 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1156\n",
"INFO:causalml: RMSE (Treatment): 1.0473\n",
"INFO:causalml: sMAPE (Control): 0.5300\n",
"INFO:causalml: sMAPE (Treatment): 0.1864\n",
"INFO:causalml: Gini (Control): 0.7404\n",
"INFO:causalml: Gini (Treatment): 0.9859\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"517 -1.249075 0.028192 -0.076209 1.228302 -0.889615 -0.970315 1.053813 \n",
"1 -0.598908 0.552677 -0.987892 1.753865 -1.047326 0.827995 0.683017 \n",
"401 -1.902249 1.609008 -1.650454 1.139514 -0.243655 0.337020 0.009184 \n",
"305 -2.290212 2.079516 1.762446 -1.346023 -0.321697 -0.296516 1.240990 \n",
"219 -0.424078 -0.746495 -0.269375 1.026374 0.569689 -2.044241 0.193521 \n",
".. ... ... ... ... ... ... ... \n",
"367 -1.119150 0.790038 -2.083099 0.272993 -2.106983 0.511889 -0.991478 \n",
"124 -0.979415 2.388193 0.461914 0.399532 0.582675 -0.175566 -0.247623 \n",
"857 -0.958933 0.848727 -0.274051 -0.994394 -0.419030 -0.062599 -0.356535 \n",
"946 -0.035807 1.000916 0.683003 1.705651 -0.522502 -1.759322 1.789445 \n",
"421 -2.133503 0.248461 -0.560940 -0.426672 -0.211597 0.784156 -1.712237 \n",
"\n",
" X0 X1 \n",
"517 1.053813 -0.970315 \n",
"1 0.683017 0.827995 \n",
"401 0.009184 0.337020 \n",
"305 1.240990 -0.296516 \n",
"219 0.193521 -2.044241 \n",
".. ... ... \n",
"367 -0.991478 0.511889 \n",
"124 -0.247623 -0.175566 \n",
"857 -0.356535 -0.062599 \n",
"946 1.789445 -1.759322 \n",
"421 -1.712237 0.784156 \n",
"\n",
"[1000 rows x 9 columns], 'y': 517 -4.981074\n",
"1 11.125450\n",
"401 7.798611\n",
"305 7.299002\n",
"219 8.839168\n",
" ... \n",
"367 -8.836089\n",
"124 10.147994\n",
"857 4.361211\n",
"946 16.526801\n",
"421 -2.608507\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 517 False\n",
"1 True\n",
"401 True\n",
"305 True\n",
"219 True\n",
" ... \n",
"367 False\n",
"124 True\n",
"857 True\n",
"946 True\n",
"421 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0421\n",
"INFO:causalml: RMSE (Treatment): 1.0731\n",
"INFO:causalml: sMAPE (Control): 0.5423\n",
"INFO:causalml: sMAPE (Treatment): 0.1840\n",
"INFO:causalml: Gini (Control): 0.7539\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"724 -1.186624 -0.204969 0.236915 0.421616 -0.619602 0.457596 0.993188 \n",
"386 0.351171 2.087307 2.341325 0.002534 -1.115900 1.480497 -0.390224 \n",
"53 -2.973900 0.733783 -0.604958 0.645836 -0.335077 -1.900429 2.401266 \n",
"298 -1.414364 1.106476 0.190108 -2.115642 -1.068247 0.043662 0.829619 \n",
"365 -0.035523 -0.341759 0.678484 0.982625 0.282776 -1.789647 1.250139 \n",
".. ... ... ... ... ... ... ... \n",
"217 -1.002155 0.954097 0.201586 0.415424 -1.836686 -1.940836 -0.566890 \n",
"480 -0.434488 0.173672 0.399111 1.491822 0.365539 0.256138 0.889867 \n",
"39 -0.999988 1.230452 -1.692300 -0.961869 -1.179618 0.079984 1.729389 \n",
"327 -1.328455 1.163397 -1.333196 1.018846 -2.697315 -1.532378 0.907498 \n",
"318 -0.506408 -0.658883 -0.359665 -0.030377 1.475936 -0.510247 -0.247610 \n",
"\n",
" X0 X1 \n",
"724 0.993188 0.457596 \n",
"386 -0.390224 1.480497 \n",
"53 2.401266 -1.900429 \n",
"298 0.829619 0.043662 \n",
"365 1.250139 -1.789647 \n",
".. ... ... \n",
"217 -0.566890 -1.940836 \n",
"480 0.889867 0.256138 \n",
"39 1.729389 0.079984 \n",
"327 0.907498 -1.532378 \n",
"318 -0.247610 -0.510247 \n",
"\n",
"[1000 rows x 9 columns], 'y': 724 7.856564\n",
"386 11.776341\n",
"53 7.558222\n",
"298 5.375935\n",
"365 15.195618\n",
" ... \n",
"217 -0.259780\n",
"480 15.267030\n",
"39 -6.109185\n",
"327 1.918859\n",
"318 10.068543\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 724 True\n",
"386 True\n",
"53 True\n",
"298 True\n",
"365 True\n",
" ... \n",
"217 True\n",
"480 True\n",
"39 False\n",
"327 True\n",
"318 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Treatment): 0.9853\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9553\n",
"INFO:causalml: RMSE (Treatment): 0.9546\n",
"INFO:causalml: sMAPE (Control): 0.5796\n",
"INFO:causalml: sMAPE (Treatment): 0.1537\n",
"INFO:causalml: Gini (Control): 0.7471\n",
"INFO:causalml: Gini (Treatment): 0.9884\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"207 -1.366234 1.181501 1.211633 0.365055 1.568180 -2.136057 0.943552 \n",
"469 -1.830332 0.130032 0.392897 -1.851040 -0.910172 -1.795773 2.143216 \n",
"210 -0.290128 0.926269 1.449719 1.449915 0.314409 -1.436922 -1.237186 \n",
"477 -2.716049 0.919646 -0.058762 1.140785 -1.477922 -1.039523 1.901640 \n",
"587 -0.913343 -0.338004 0.824561 1.785646 -0.630247 -0.794141 1.563777 \n",
".. ... ... ... ... ... ... ... \n",
"358 -1.690018 0.297790 -0.583945 1.387763 -1.053933 -0.484870 0.193280 \n",
"998 -0.132933 0.842204 -1.539580 -0.012722 -1.320756 0.959635 1.889926 \n",
"262 -1.701307 -1.234552 -0.291712 1.306074 0.636205 -0.594167 0.592119 \n",
"846 -1.812872 -0.339279 0.842863 0.273237 0.789526 1.046829 1.325270 \n",
"295 -0.991002 1.251465 -1.432965 0.507186 -1.377766 0.438091 1.324539 \n",
"\n",
" X0 X1 \n",
"207 0.943552 -2.136057 \n",
"469 2.143216 -1.795773 \n",
"210 -1.237186 -1.436922 \n",
"477 1.901640 -1.039523 \n",
"587 1.563777 -0.794141 \n",
".. ... ... \n",
"358 0.193280 -0.484870 \n",
"998 1.889926 0.959635 \n",
"262 0.592119 -0.594167 \n",
"846 1.325270 1.046829 \n",
"295 1.324539 0.438091 \n",
"\n",
"[1000 rows x 9 columns], 'y': 207 13.068250\n",
"469 4.435916\n",
"210 7.048680\n",
"477 7.037455\n",
"587 12.208485\n",
" ... \n",
"358 3.577322\n",
"998 13.321122\n",
"262 7.461448\n",
"846 12.705539\n",
"295 9.933260\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 207 True\n",
"469 True\n",
"210 True\n",
"477 True\n",
"587 True\n",
" ... \n",
"358 True\n",
"998 True\n",
"262 True\n",
"846 True\n",
"295 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1203\n",
"INFO:causalml: RMSE (Treatment): 0.9173\n",
"INFO:causalml: sMAPE (Control): 0.5700\n",
"INFO:causalml: sMAPE (Treatment): 0.1799\n",
"INFO:causalml: Gini (Control): 0.7873\n",
"INFO:causalml: Gini (Treatment): 0.9906\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0168\n",
"INFO:causalml: RMSE (Treatment): 0.9727\n",
"INFO:causalml: sMAPE (Control): 0.6179\n",
"INFO:causalml: sMAPE (Treatment): 0.1711\n",
"INFO:causalml: Gini (Control): 0.7324\n",
"INFO:causalml: Gini (Treatment): 0.9886\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"969 -1.335965 0.447648 -0.411083 1.979017 -0.138700 2.367625 1.304766 \n",
"120 0.878535 1.143899 -0.483360 1.389960 -1.129051 -0.036144 0.322280 \n",
"398 -0.544874 1.460350 -1.210807 0.246759 -1.896953 -1.364403 0.295212 \n",
"962 0.011323 0.505232 -0.851645 -0.691518 -0.815307 -2.475926 -0.811474 \n",
"969 -1.471246 0.725484 -0.285822 1.968396 0.098097 2.035384 1.534909 \n",
".. ... ... ... ... ... ... ... \n",
"156 -2.062858 1.654820 -0.039749 0.633792 -1.023244 -0.324959 1.014639 \n",
"996 -2.996934 1.835326 -0.415804 1.731529 1.114039 1.000899 1.966124 \n",
"283 -2.445636 0.615026 -0.362207 -0.613346 1.228393 -1.540628 0.829009 \n",
"15 -2.063208 1.203270 0.445253 0.477875 -2.216014 -0.031189 0.820581 \n",
"590 -1.171838 0.232455 0.198807 1.170847 0.340350 0.982074 1.019249 \n",
"\n",
" X0 X1 \n",
"969 1.304766 2.367625 \n",
"120 0.322280 -0.036144 \n",
"398 0.295212 -1.364403 \n",
"962 -0.811474 -2.475926 \n",
"969 1.534909 2.035384 \n",
".. ... ... \n",
"156 1.014639 -0.324959 \n",
"996 1.966124 1.000899 \n",
"283 0.829009 -1.540628 \n",
"15 0.820581 -0.031189 \n",
"590 1.019249 0.982074 \n",
"\n",
"[1000 rows x 9 columns], 'y': 969 17.059856\n",
"120 13.587443\n",
"398 -5.105764\n",
"962 1.353868\n",
"969 17.059856\n",
" ... \n",
"156 6.574194\n",
"996 17.211685\n",
"283 7.081134\n",
"15 2.255591\n",
"590 13.717742\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 969 True\n",
"120 True\n",
"398 False\n",
"962 True\n",
"969 True\n",
" ... \n",
"156 True\n",
"996 True\n",
"283 True\n",
"15 True\n",
"590 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"762 0.106489 0.709110 -0.977151 0.408870 -0.997734 0.051829 2.205049 \n",
"561 -0.582679 -0.252048 -0.449171 1.288204 -2.428243 0.213163 1.199164 \n",
"771 -1.581360 0.727476 0.228220 0.436623 0.611546 1.617179 2.021509 \n",
"457 -1.325958 1.534327 -0.725483 1.753939 -2.868028 -1.354123 0.004881 \n",
"665 -2.154642 0.336701 -0.127727 -0.622155 0.322338 -0.912627 -0.105646 \n",
".. ... ... ... ... ... ... ... \n",
"496 -0.207431 0.517641 -0.880719 1.426367 -0.957096 -0.597891 -0.460067 \n",
"761 -1.782044 -0.230445 -0.712217 -1.423460 0.959061 -0.572837 1.033045 \n",
"799 -1.173348 0.516625 1.728205 0.736736 -0.216253 0.221584 3.380923 \n",
"456 -1.039482 1.682213 0.772552 1.290649 0.140001 0.468885 0.540177 \n",
"489 -0.254097 -1.425958 -0.688945 0.426641 0.148577 -0.933015 -1.321576 \n",
"\n",
" X0 X1 \n",
"762 2.205049 0.051829 \n",
"561 1.199164 0.213163 \n",
"771 2.021509 1.617179 \n",
"457 0.004881 -1.354123 \n",
"665 -0.105646 -0.912627 \n",
".. ... ... \n",
"496 -0.460067 -0.597891 \n",
"761 1.033045 -0.572837 \n",
"799 3.380923 0.221584 \n",
"456 0.540177 0.468885 \n",
"489 -1.321576 -0.933015 \n",
"\n",
"[1000 rows x 9 columns], 'y': 762 17.175897\n",
"561 8.271140\n",
"771 18.593729\n",
"457 -8.028104\n",
"665 2.331887\n",
" ... \n",
"496 5.574781\n",
"761 7.959767\n",
"799 21.395850\n",
"456 12.752919\n",
"489 2.992182\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 762 True\n",
"561 True\n",
"771 True\n",
"457 False\n",
"665 True\n",
" ... \n",
"496 True\n",
"761 True\n",
"799 True\n",
"456 True\n",
"489 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0600\n",
"INFO:causalml: RMSE (Treatment): 0.9813\n",
"INFO:causalml: sMAPE (Control): 0.5765\n",
"INFO:causalml: sMAPE (Treatment): 0.1614\n",
"INFO:causalml: Gini (Control): 0.7312\n",
"INFO:causalml: Gini (Treatment): 0.9879\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0749\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"982 -0.794501 0.663315 -0.444807 -0.869413 0.874432 -1.410284 1.245162 \n",
"154 -1.522914 2.547789 0.958373 -0.742162 0.153355 0.081442 0.369015 \n",
"810 -0.413913 0.523270 0.026244 0.698672 -0.373440 -0.178206 -0.843674 \n",
"130 -0.300918 0.222057 -1.254998 1.360267 -0.882821 -0.874269 0.547756 \n",
"268 0.108036 -0.440682 0.323611 0.832749 -0.207856 -1.020580 -1.452514 \n",
".. ... ... ... ... ... ... ... \n",
"934 -2.424483 1.343930 -0.123170 1.154069 -1.879578 -1.717017 1.443784 \n",
"818 -2.141950 1.077866 -0.176088 0.295325 -2.526841 -0.678777 0.773618 \n",
"732 -1.838668 1.864397 -0.393074 0.704162 -0.116134 -0.980395 2.071491 \n",
"646 -0.691894 0.965563 0.295157 1.679703 -1.580214 -0.537919 0.595561 \n",
"679 -1.193441 1.345853 0.874096 -0.725893 -1.256080 0.695600 0.692764 \n",
"\n",
" X0 X1 \n",
"982 1.245162 -1.410284 \n",
"154 0.369015 0.081442 \n",
"810 -0.843674 -0.178206 \n",
"130 0.547756 -0.874269 \n",
"268 -1.452514 -1.020580 \n",
".. ... ... \n",
"934 1.443784 -1.717017 \n",
"818 0.773618 -0.678777 \n",
"732 2.071491 -0.980395 \n",
"646 0.595561 -0.537919 \n",
"679 0.692764 0.695600 \n",
"\n",
"[1000 rows x 9 columns], 'y': 982 12.003112\n",
"154 9.367612\n",
"810 5.956647\n",
"130 9.234615\n",
"268 3.465358\n",
" ... \n",
"934 2.331482\n",
"818 -11.770683\n",
"732 -3.133831\n",
"646 9.399353\n",
"679 6.809382\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 982 True\n",
"154 True\n",
"810 True\n",
"130 True\n",
"268 True\n",
" ... \n",
"934 True\n",
"818 False\n",
"732 False\n",
"646 True\n",
"679 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"332 -1.296487 0.315159 -0.917615 1.194753 0.499105 0.293720 1.602796 \n",
"344 -2.449548 1.486259 -0.160623 -0.389842 -1.081393 -0.050959 1.996267 \n",
"486 -0.270020 -0.394789 -0.572976 0.377000 0.966363 -1.171396 0.454287 \n",
"23 -1.579551 0.179087 -0.682078 0.198108 0.152373 0.692088 2.098798 \n",
"873 -0.179652 -0.287899 0.680093 0.701952 -1.191745 -1.905421 0.095141 \n",
".. ... ... ... ... ... ... ... \n",
"241 -0.858343 2.347961 -0.871282 1.778801 0.129180 -2.613692 -0.315300 \n",
"991 -0.297982 0.682839 0.031873 -0.980435 -2.292614 -0.267537 2.225250 \n",
"737 -0.361140 0.059002 -0.221684 0.200752 -1.620890 -1.393805 1.183499 \n",
"903 -2.201737 1.581319 0.868458 0.735588 0.787632 1.289393 0.203314 \n",
"926 1.129385 0.557172 -0.624555 1.080902 -0.720518 -0.244517 0.376367 \n",
"\n",
" X0 X1 \n",
"332 1.602796 0.293720 \n",
"344 1.996267 -0.050959 \n",
"486 0.454287 -1.171396 \n",
"23 2.098798 0.692088 \n",
"873 0.095141 -1.905421 \n",
".. ... ... \n",
"241 -0.315300 -2.613692 \n",
"991 2.225250 -0.267537 \n",
"737 1.183499 -1.393805 \n",
"903 0.203314 1.289393 \n",
"926 0.376367 -0.244517 \n",
"\n",
"[1000 rows x 9 columns], 'y': 332 16.296656\n",
"344 -9.322258\n",
"486 12.711964\n",
"23 -4.601417\n",
"873 4.499265\n",
" ... \n",
"241 7.793042\n",
"991 -7.648859\n",
"737 6.679250\n",
"903 11.446682\n",
"926 13.032000\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 332 True\n",
"344 False\n",
"486 True\n",
"23 False\n",
"873 True\n",
" ... \n",
"241 True\n",
"991 False\n",
"737 True\n",
"903 True\n",
"926 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Treatment): 1.1122\n",
"INFO:causalml: sMAPE (Control): 0.5652\n",
"INFO:causalml: sMAPE (Treatment): 0.2127\n",
"INFO:causalml: Gini (Control): 0.7542\n",
"INFO:causalml: Gini (Treatment): 0.9839\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1086\n",
"INFO:causalml: RMSE (Treatment): 1.0436\n",
"INFO:causalml: sMAPE (Control): 0.5778\n",
"INFO:causalml: sMAPE (Treatment): 0.1765\n",
"INFO:causalml: Gini (Control): 0.6881\n",
"INFO:causalml: Gini (Treatment): 0.9868\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0219\n",
"INFO:causalml: RMSE (Treatment): 0.9830\n",
"INFO:causalml: sMAPE (Control): 0.5468\n",
"INFO:causalml: sMAPE (Treatment): 0.1661\n",
"INFO:causalml: Gini (Control): 0.7412\n",
"INFO:causalml: Gini (Treatment): 0.9872\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"738 -0.484416 1.687269 -1.583798 2.053387 -0.903056 -2.357297 0.762175 \n",
"403 -1.774697 2.419671 0.581029 0.345242 -2.182253 -0.431024 -1.477473 \n",
"738 -0.589523 1.673371 -1.640816 2.072849 -0.761020 -2.556119 0.647355 \n",
"398 -0.430815 1.459252 -1.310444 0.477303 -1.911626 -1.131070 0.406184 \n",
"540 -1.320015 1.915577 -0.459789 -0.082961 2.080044 0.673359 1.359615 \n",
".. ... ... ... ... ... ... ... \n",
"185 -0.189739 0.798016 -1.637180 -0.973693 0.515212 -0.479969 -0.733242 \n",
"903 -2.059504 1.688765 1.129595 0.414466 0.812262 1.673828 0.329361 \n",
"303 -1.732982 1.160460 0.179380 1.219356 0.864191 -0.416468 0.937511 \n",
"224 -0.243703 1.430595 -0.902650 -0.183798 -0.514459 -0.550796 1.359030 \n",
"216 -1.143249 1.699965 -2.537634 -0.834504 -0.963018 -1.343873 0.012237 \n",
"\n",
" X0 X1 \n",
"738 0.762175 -2.357297 \n",
"403 -1.477473 -0.431024 \n",
"738 0.647355 -2.556119 \n",
"398 0.406184 -1.131070 \n",
"540 1.359615 0.673359 \n",
".. ... ... \n",
"185 -0.733242 -0.479969 \n",
"903 0.329361 1.673828 \n",
"303 0.937511 -0.416468 \n",
"224 1.359030 -0.550796 \n",
"216 0.012237 -1.343873 \n",
"\n",
"[1000 rows x 9 columns], 'y': 738 8.470271\n",
"403 -3.265557\n",
"738 8.470271\n",
"398 -5.105764\n",
"540 19.517995\n",
" ... \n",
"185 -0.302411\n",
"903 11.446682\n",
"303 11.687300\n",
"224 -0.964343\n",
"216 2.376140\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 738 True\n",
"403 True\n",
"738 True\n",
"398 False\n",
"540 True\n",
" ... \n",
"185 False\n",
"903 True\n",
"303 True\n",
"224 False\n",
"216 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"172 -1.043971 1.761026 -1.454093 -0.200212 -0.640173 -1.525210 1.509516 \n",
"136 -1.924297 -0.274434 -0.917478 1.109700 -0.054534 0.789436 -0.133145 \n",
"299 -1.192639 0.104373 -0.938420 1.706595 0.154327 -1.158545 -0.509465 \n",
"13 -1.269414 0.227623 0.692321 -2.352126 -1.858097 -0.389351 -0.227835 \n",
"876 -0.757699 -0.034899 -0.510357 0.536978 -0.035272 -1.700392 2.046986 \n",
".. ... ... ... ... ... ... ... \n",
"540 -1.397708 1.894398 -0.492258 0.041512 2.224414 0.784732 1.189514 \n",
"154 -1.644824 2.600395 0.678450 -0.861557 -0.025366 0.179584 0.538859 \n",
"678 0.626320 0.739785 -0.184908 0.363249 -1.507676 -0.041604 1.191561 \n",
"888 -0.079391 0.649108 -1.904946 1.743524 0.441493 -0.063658 1.094585 \n",
"681 -0.651375 1.471942 0.441373 -1.142466 1.357624 -0.002035 1.670379 \n",
"\n",
" X0 X1 \n",
"172 1.509516 -1.525210 \n",
"136 -0.133145 0.789436 \n",
"299 -0.509465 -1.158545 \n",
"13 -0.227835 -0.389351 \n",
"876 2.046986 -1.700392 \n",
".. ... ... \n",
"540 1.189514 0.784732 \n",
"154 0.538859 0.179584 \n",
"678 1.191561 -0.041604 \n",
"888 1.094585 -0.063658 \n",
"681 1.670379 -0.002035 \n",
"\n",
"[1000 rows x 9 columns], 'y': 172 10.160763\n",
"136 -4.977571\n",
"299 5.614664\n",
"13 -2.020044\n",
"876 12.555557\n",
" ... \n",
"540 19.517995\n",
"154 9.367612\n",
"678 13.061989\n",
"888 16.906677\n",
"681 18.090454\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 172 True\n",
"136 False\n",
"299 True\n",
"13 True\n",
"876 True\n",
" ... \n",
"540 True\n",
"154 True\n",
"678 True\n",
"888 True\n",
"681 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.2539\n",
"INFO:causalml: RMSE (Treatment): 0.9791\n",
"INFO:causalml: sMAPE (Control): 0.5269\n",
"INFO:causalml: sMAPE (Treatment): 0.1816\n",
"INFO:causalml: Gini (Control): 0.7565\n",
"INFO:causalml: Gini (Treatment): 0.9892\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0712\n",
"INFO:causalml: RMSE (Treatment): 0.9911\n",
"INFO:causalml: sMAPE (Control): 0.5510\n",
"INFO:causalml: sMAPE (Treatment): 0.1923\n",
"INFO:causalml: Gini (Control): 0.7424\n",
"INFO:causalml: Gini (Treatment): 0.9883\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"770 -2.833607 0.247946 -1.054563 -0.451676 -1.223193 0.524625 1.020635 \n",
"805 -2.511877 -0.416616 -0.952440 -0.180083 0.205770 -2.841067 0.262597 \n",
"483 -1.372015 0.849386 -0.293284 0.759140 -2.075523 0.228387 0.476496 \n",
"396 -0.147135 -1.883991 -1.230722 -1.231061 -1.361075 0.330904 0.628008 \n",
"110 -1.689077 2.282338 -0.370636 1.396179 -0.356844 0.761223 0.814718 \n",
".. ... ... ... ... ... ... ... \n",
"196 -1.900765 1.142843 -0.900225 -0.632667 -1.317238 -1.090468 0.725665 \n",
"804 -2.456427 0.834854 -0.079465 1.288538 -1.198145 -0.950941 0.385082 \n",
"427 0.382613 -0.025500 0.603087 1.308133 -1.791121 -0.510804 2.260320 \n",
"371 -2.489665 1.949779 -0.742450 -0.413601 -0.449260 0.283632 0.206128 \n",
"718 -1.214701 1.204254 -2.714157 -0.823141 0.496854 -2.151207 0.308420 \n",
"\n",
" X0 X1 \n",
"770 1.020635 0.524625 \n",
"805 0.262597 -2.841067 \n",
"483 0.476496 0.228387 \n",
"396 0.628008 0.330904 \n",
"110 0.814718 0.761223 \n",
".. ... ... \n",
"196 0.725665 -1.090468 \n",
"804 0.385082 -0.950941 \n",
"427 2.260320 -0.510804 \n",
"371 0.206128 0.283632 \n",
"718 0.308420 -2.151207 \n",
"\n",
"[1000 rows x 9 columns], 'y': 770 -12.067607\n",
"805 -0.721618\n",
"483 5.198246\n",
"396 -9.105144\n",
"110 11.934711\n",
" ... \n",
"196 1.676450\n",
"804 2.906483\n",
"427 15.122153\n",
"371 4.468847\n",
"718 -3.300467\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 770 False\n",
"805 True\n",
"483 True\n",
"396 False\n",
"110 True\n",
" ... \n",
"196 True\n",
"804 True\n",
"427 True\n",
"371 True\n",
"718 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"563 -0.268443 -0.219708 -2.876535 1.271821 -1.312667 1.280986 0.323887 \n",
"106 -1.459605 1.569425 0.082599 -0.292869 -1.835696 -2.024142 1.050100 \n",
"223 -0.733866 0.529961 -0.028252 1.931603 -1.823669 -0.976670 0.713833 \n",
"27 -0.859815 0.867771 -1.343087 -0.258287 -0.528599 -1.559249 1.140061 \n",
"960 0.292158 -0.143984 -0.053099 -0.938650 -3.131975 -0.574136 1.483128 \n",
".. ... ... ... ... ... ... ... \n",
"7 0.169050 1.664113 -1.617550 0.850336 -1.624987 -0.440930 0.929542 \n",
"961 -1.645919 1.515655 -0.383109 1.373957 -0.598925 -2.405242 0.310820 \n",
"818 -2.336560 1.184435 -0.133590 0.300783 -2.270370 -0.602481 0.975037 \n",
"163 -0.801707 0.799981 0.357547 0.710283 -0.733089 -1.185072 0.890325 \n",
"408 -0.694694 1.651812 -0.792777 0.006787 2.315751 -1.067453 -1.079735 \n",
"\n",
" X0 X1 \n",
"563 0.323887 1.280986 \n",
"106 1.050100 -2.024142 \n",
"223 0.713833 -0.976670 \n",
"27 1.140061 -1.559249 \n",
"960 1.483128 -0.574136 \n",
".. ... ... \n",
"7 0.929542 -0.440930 \n",
"961 0.310820 -2.405242 \n",
"818 0.975037 -0.602481 \n",
"163 0.890325 -1.185072 \n",
"408 -1.079735 -1.067453 \n",
"\n",
"[1000 rows x 9 columns], 'y': 563 -5.142521\n",
"106 -8.631314\n",
"223 6.636087\n",
"27 -3.423349\n",
"960 4.643805\n",
" ... \n",
"7 -2.646268\n",
"961 4.732956\n",
"818 -11.770683\n",
"163 8.634512\n",
"408 10.095428\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 563 False\n",
"106 False\n",
"223 True\n",
"27 False\n",
"960 True\n",
" ... \n",
"7 False\n",
"961 True\n",
"818 False\n",
"163 True\n",
"408 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.2491\n",
"INFO:causalml: RMSE (Treatment): 1.1114\n",
"INFO:causalml: sMAPE (Control): 0.5319\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"962 -0.009677 0.669116 -0.849799 -0.702391 -0.940493 -2.525755 -0.891971 \n",
"391 -0.341418 0.951311 1.421517 -1.064450 0.707648 0.191544 0.527653 \n",
"233 0.198885 1.069432 -1.260303 0.733528 0.155105 -1.662024 2.801471 \n",
"123 -0.912546 1.504504 -0.369505 1.432430 -2.599077 1.204453 -1.314089 \n",
"91 -1.832020 1.182525 -0.244803 -0.953757 0.600936 -0.634880 0.917622 \n",
".. ... ... ... ... ... ... ... \n",
"461 -1.360427 -1.914816 0.510510 0.500077 -0.493602 0.787010 0.798915 \n",
"957 -0.061057 2.683942 -0.213917 0.549708 -1.910918 0.674554 -0.616028 \n",
"385 0.122655 -0.360919 -0.231994 0.386600 -1.906796 -1.320416 -1.640399 \n",
"773 -1.300000 -0.390972 -0.684988 -0.244824 -0.678865 -1.006887 -0.164986 \n",
"59 -2.485758 0.450724 -0.618499 -0.465156 -1.369240 -1.414008 -1.160067 \n",
"\n",
" X0 X1 \n",
"962 -0.891971 -2.525755 \n",
"391 0.527653 0.191544 \n",
"233 2.801471 -1.662024 \n",
"123 -1.314089 1.204453 \n",
"91 0.917622 -0.634880 \n",
".. ... ... \n",
"461 0.798915 0.787010 \n",
"957 -0.616028 0.674554 \n",
"385 -1.640399 -1.320416 \n",
"773 -0.164986 -1.006887 \n",
"59 -1.160067 -1.414008 \n",
"\n",
"[1000 rows x 9 columns], 'y': 962 1.353868\n",
"391 13.502571\n",
"233 21.285516\n",
"123 1.168663\n",
"91 9.048832\n",
" ... \n",
"461 -6.434741\n",
"957 6.531621\n",
"385 -1.265529\n",
"773 1.630931\n",
"59 -11.229570\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 962 True\n",
"391 True\n",
"233 True\n",
"123 True\n",
"91 True\n",
" ... \n",
"461 False\n",
"957 True\n",
"385 True\n",
"773 True\n",
"59 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Treatment): 0.2131\n",
"INFO:causalml: Gini (Control): 0.7332\n",
"INFO:causalml: Gini (Treatment): 0.9857\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9914\n",
"INFO:causalml: RMSE (Treatment): 1.0505\n",
"INFO:causalml: sMAPE (Control): 0.5377\n",
"INFO:causalml: sMAPE (Treatment): 0.1699\n",
"INFO:causalml: Gini (Control): 0.7488\n",
"INFO:causalml: Gini (Treatment): 0.9845\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"404 0.304284 -1.498603 -0.480307 1.267006 -0.620078 0.781348 -0.391429 \n",
"910 0.456531 1.361552 -1.515220 0.848143 1.245427 -0.631175 1.084272 \n",
"592 -1.825880 1.633655 -1.557530 0.061253 -0.771143 0.575775 1.090394 \n",
"820 -0.073384 1.163020 -1.955732 1.416701 -2.601253 1.077416 -0.101785 \n",
"586 -1.843453 1.412223 -0.709723 0.477786 -1.890833 0.265785 2.900704 \n",
".. ... ... ... ... ... ... ... \n",
"665 -2.203587 0.162848 -0.052461 -0.901767 0.184596 -0.682588 0.078397 \n",
"308 -1.094821 0.338785 0.692682 0.770982 1.073404 -0.773410 0.837814 \n",
"624 -2.036389 -0.353131 -0.077377 1.111249 -1.337650 0.085889 0.918850 \n",
"588 -1.013102 -0.409262 0.759175 0.322997 -1.904446 -1.039943 0.126042 \n",
"569 -2.779166 0.520614 0.576555 1.902577 1.473554 -1.378417 2.415496 \n",
"\n",
" X0 X1 \n",
"404 -0.391429 0.781348 \n",
"910 1.084272 -0.631175 \n",
"592 1.090394 0.575775 \n",
"820 -0.101785 1.077416 \n",
"586 2.900704 0.265785 \n",
".. ... ... \n",
"665 0.078397 -0.682588 \n",
"308 0.837814 -0.773410 \n",
"624 0.918850 0.085889 \n",
"588 0.126042 -1.039943 \n",
"569 2.415496 -1.378417 \n",
"\n",
"[1000 rows x 9 columns], 'y': 404 -1.323791\n",
"910 20.169101\n",
"592 7.749983\n",
"820 4.660715\n",
"586 -8.767996\n",
" ... \n",
"665 2.331887\n",
"308 12.604981\n",
"624 5.810979\n",
"588 -7.223205\n",
"569 16.043709\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 404 False\n",
"910 True\n",
"592 True\n",
"820 True\n",
"586 False\n",
" ... \n",
"665 True\n",
"308 True\n",
"624 True\n",
"588 False\n",
"569 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9916\n",
"INFO:causalml: RMSE (Treatment): 1.0002\n",
"INFO:causalml: sMAPE (Control): 0.5286\n",
"INFO:causalml: sMAPE (Treatment): 0.1753\n",
"INFO:causalml: Gini (Control): 0.7347\n",
"INFO:causalml: Gini (Treatment): 0.9868\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1868\n",
"INFO:causalml: RMSE (Treatment): 1.0344\n",
"INFO:causalml: sMAPE (Control): 0.5700\n",
"INFO:causalml: sMAPE (Treatment): 0.1891\n",
"INFO:causalml: Gini (Control): 0.7466\n",
"INFO:causalml: Gini (Treatment): 0.9874\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"234 0.277636 -0.458319 -1.236754 1.580579 -1.049608 -1.092289 0.073106 \n",
"892 -1.171172 0.806907 -0.680481 -0.663835 -1.156163 -0.919481 -0.952076 \n",
"671 -1.789504 2.029038 0.366626 0.671937 -1.483687 -0.112850 -0.811123 \n",
"833 0.263368 0.184965 0.666979 1.310278 -0.849572 -1.298711 1.324894 \n",
"304 -0.859428 0.727926 1.667976 2.031498 1.015084 0.994497 1.432807 \n",
".. ... ... ... ... ... ... ... \n",
"564 -0.722416 -0.238189 -0.521413 1.038525 -0.210707 0.310418 2.076362 \n",
"894 -0.051398 0.530460 -2.565525 0.448468 -0.949153 -0.288395 1.726858 \n",
"868 -1.754745 1.130944 -0.308641 -0.052623 -0.531785 -0.459450 1.764856 \n",
"590 -1.432875 0.242315 -0.043110 1.001279 0.420302 1.061977 1.081231 \n",
"527 -1.840716 -0.321949 -2.442757 0.317798 0.812937 0.094385 0.851838 \n",
"\n",
" X0 X1 \n",
"234 0.073106 -1.092289 \n",
"892 -0.952076 -0.919481 \n",
"671 -0.811123 -0.112850 \n",
"833 1.324894 -1.298711 \n",
"304 1.432807 0.994497 \n",
".. ... ... \n",
"564 2.076362 0.310418 \n",
"894 1.726858 -0.288395 \n",
"868 1.764856 -0.459450 \n",
"590 1.081231 1.061977 \n",
"527 0.851838 0.094385 \n",
"\n",
"[1000 rows x 9 columns], 'y': 234 7.980017\n",
"892 -0.915984\n",
"671 -6.150284\n",
"833 13.445887\n",
"304 22.259686\n",
" ... \n",
"564 16.574010\n",
"894 -2.776312\n",
"868 -6.358042\n",
"590 13.717742\n",
"527 8.043131\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 234 True\n",
"892 True\n",
"671 False\n",
"833 True\n",
"304 True\n",
" ... \n",
"564 True\n",
"894 False\n",
"868 False\n",
"590 True\n",
"527 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"892 -1.365191 0.809145 -0.651818 -0.533120 -1.174905 -0.754857 -0.953927 \n",
"478 -1.009846 1.403938 -1.112725 0.483925 0.288028 -2.041947 1.490763 \n",
"147 -2.033595 2.003972 -2.905970 -0.874470 0.918767 -0.748644 1.628621 \n",
"22 0.468947 1.163442 0.184800 3.538828 -0.021932 1.220487 2.210038 \n",
"955 -0.411135 0.547188 -1.517731 1.258115 1.903441 -0.868937 1.334142 \n",
".. ... ... ... ... ... ... ... \n",
"503 -1.932080 2.001397 -1.569077 0.379106 0.276418 -0.071977 0.991890 \n",
"486 -0.153314 -0.096854 -0.783739 0.325289 1.050841 -0.886658 0.377008 \n",
"848 -1.888484 1.311574 -2.408030 0.264099 -0.420793 -1.231687 0.327405 \n",
"723 -0.168558 1.249420 0.753321 0.597698 -0.693478 -0.348529 0.523999 \n",
"78 -1.157900 0.135025 0.432163 1.809337 -2.251005 -0.938530 -0.140407 \n",
"\n",
" X0 X1 \n",
"892 -0.953927 -0.754857 \n",
"478 1.490763 -2.041947 \n",
"147 1.628621 -0.748644 \n",
"22 2.210038 1.220487 \n",
"955 1.334142 -0.868937 \n",
".. ... ... \n",
"503 0.991890 -0.071977 \n",
"486 0.377008 -0.886658 \n",
"848 0.327405 -1.231687 \n",
"723 0.523999 -0.348529 \n",
"78 -0.140407 -0.938530 \n",
"\n",
"[1000 rows x 9 columns], 'y': 892 -0.915984\n",
"478 11.134846\n",
"147 10.091404\n",
"22 25.918390\n",
"955 18.503792\n",
" ... \n",
"503 -3.163402\n",
"486 12.711964\n",
"848 -5.747920\n",
"723 12.592456\n",
"78 2.567970\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 892 True\n",
"478 True\n",
"147 True\n",
"22 True\n",
"955 True\n",
" ... \n",
"503 False\n",
"486 True\n",
"848 False\n",
"723 True\n",
"78 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1447\n",
"INFO:causalml: RMSE (Treatment): 0.9871\n",
"INFO:causalml: sMAPE (Control): 0.5226\n",
"INFO:causalml: sMAPE (Treatment): 0.1729\n",
"INFO:causalml: Gini (Control): 0.6911\n",
"INFO:causalml: Gini (Treatment): 0.9871\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"192 0.013481 0.077942 -0.358307 0.168147 -0.467291 -0.693907 0.667007 \n",
"417 -0.366190 0.259755 -1.225215 -0.106444 -0.371682 -1.259310 1.718957 \n",
"799 -1.223681 0.573615 1.814878 0.681714 -0.181330 0.214086 3.431480 \n",
"286 -0.799104 1.123722 0.147207 0.723531 -1.327644 0.238024 0.305314 \n",
"498 0.729111 1.362158 0.281377 0.617431 -0.087265 0.119880 0.608881 \n",
".. ... ... ... ... ... ... ... \n",
"117 -2.231882 0.665366 -1.909693 -1.065803 -1.313906 -0.629881 0.962470 \n",
"792 -0.774205 0.254451 -0.106932 -1.628313 -0.065050 -0.218405 1.323578 \n",
"253 -0.250339 0.511999 -0.172158 1.445175 -1.914054 0.621038 0.901125 \n",
"291 0.333775 -0.137968 -0.339481 0.150972 -0.486684 0.052764 2.652309 \n",
"463 -1.375221 1.114778 -1.084832 0.642387 0.909748 0.993138 0.657208 \n",
"\n",
" X0 X1 \n",
"192 0.667007 -0.693907 \n",
"417 1.718957 -1.259310 \n",
"799 3.431480 0.214086 \n",
"286 0.305314 0.238024 \n",
"498 0.608881 0.119880 \n",
".. ... ... \n",
"117 0.962470 -0.629881 \n",
"792 1.323578 -0.218405 \n",
"253 0.901125 0.621038 \n",
"291 2.652309 0.052764 \n",
"463 0.657208 0.993138 \n",
"\n",
"[1000 rows x 9 columns], 'y': 192 10.887872\n",
"417 12.283023\n",
"799 21.395850\n",
"286 7.882269\n",
"498 17.581110\n",
" ... \n",
"117 -11.180244\n",
"792 9.683267\n",
"253 11.727481\n",
"291 18.348778\n",
"463 13.719677\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 192 True\n",
"417 True\n",
"799 True\n",
"286 True\n",
"498 True\n",
" ... \n",
"117 False\n",
"792 True\n",
"253 True\n",
"291 True\n",
"463 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9856\n",
"INFO:causalml: RMSE (Treatment): 0.9593\n",
"INFO:causalml: sMAPE (Control): 0.5396\n",
"INFO:causalml: sMAPE (Treatment): 0.1518\n",
"INFO:causalml: Gini (Control): 0.7416\n",
"INFO:causalml: Gini (Treatment): 0.9867\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1492\n",
"INFO:causalml: RMSE (Treatment): 0.9369\n",
"INFO:causalml: sMAPE (Control): 0.5904\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"813 -2.574793 1.778857 -1.366494 0.361929 0.385444 -1.517934 0.547660 \n",
"414 -0.709890 0.365473 -3.032579 0.495475 -1.578610 -1.055620 2.481256 \n",
"395 -1.815771 0.441079 -0.282241 0.386440 1.087325 -1.114349 0.567645 \n",
"458 -1.367800 -0.075564 -0.649272 1.415704 -1.028008 0.048305 0.511034 \n",
"204 -1.523569 1.641104 -1.394166 0.432445 1.405679 0.444463 1.984802 \n",
".. ... ... ... ... ... ... ... \n",
"409 -0.474703 0.299137 1.351608 -0.142418 -0.348057 1.312824 2.057486 \n",
"846 -1.715347 -0.402672 0.816812 0.259425 0.681951 0.791362 1.367577 \n",
"662 -0.004893 0.721397 -1.350244 1.319241 -2.629019 -2.145902 -0.150837 \n",
"508 0.387571 0.618235 1.634774 -1.093117 1.003168 0.543222 1.559748 \n",
"490 -2.459289 2.497210 -0.739107 0.191121 0.310616 1.609913 1.238706 \n",
"\n",
" X0 X1 \n",
"813 0.547660 -1.517934 \n",
"414 2.481256 -1.055620 \n",
"395 0.567645 -1.114349 \n",
"458 0.511034 0.048305 \n",
"204 1.984802 0.444463 \n",
".. ... ... \n",
"409 2.057486 1.312824 \n",
"846 1.367577 0.791362 \n",
"662 -0.150837 -2.145902 \n",
"508 1.559748 0.543222 \n",
"490 1.238706 1.609913 \n",
"\n",
"[1000 rows x 9 columns], 'y': 813 5.390940\n",
"414 -6.908901\n",
"395 8.681410\n",
"458 7.125670\n",
"204 18.498160\n",
" ... \n",
"409 17.513846\n",
"846 12.705539\n",
"662 1.373230\n",
"508 19.084238\n",
"490 -3.562790\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 813 True\n",
"414 False\n",
"395 True\n",
"458 True\n",
"204 True\n",
" ... \n",
"409 True\n",
"846 True\n",
"662 True\n",
"508 True\n",
"490 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"491 0.170054 0.365630 -0.436193 0.121649 0.655171 -0.755025 -0.481998 \n",
"155 0.265491 2.283702 0.416064 1.689635 -0.328769 1.362896 1.004507 \n",
"644 0.192433 -0.486979 -0.263539 0.925781 -2.334375 0.092650 1.466703 \n",
"918 -1.677469 1.390816 0.913525 1.070667 -0.519165 -0.050166 1.873531 \n",
"979 -1.379922 -0.553287 0.201718 1.229420 1.009567 -0.628174 0.494019 \n",
".. ... ... ... ... ... ... ... \n",
"329 -0.807969 0.144493 0.114449 0.363988 -2.069952 0.209398 2.149941 \n",
"506 -1.793297 -0.478382 -2.362130 1.851526 -2.501745 -1.469484 0.081321 \n",
"980 -1.484445 1.470760 -1.486318 -0.797821 -1.396926 0.557199 1.886761 \n",
"678 0.627820 0.704262 -0.283240 0.380088 -1.533414 0.148136 0.994715 \n",
"175 -1.276874 0.830585 -0.634198 0.967282 0.198645 -0.826272 2.607876 \n",
"\n",
" X0 X1 \n",
"491 -0.481998 -0.755025 \n",
"155 1.004507 1.362896 \n",
"644 1.466703 0.092650 \n",
"918 1.873531 -0.050166 \n",
"979 0.494019 -0.628174 \n",
".. ... ... \n",
"329 2.149941 0.209398 \n",
"506 0.081321 -1.469484 \n",
"980 1.886761 0.557199 \n",
"678 0.994715 0.148136 \n",
"175 2.607876 -0.826272 \n",
"\n",
"[1000 rows x 9 columns], 'y': 491 8.770425\n",
"155 19.147114\n",
"644 9.476615\n",
"918 12.845128\n",
"979 10.393456\n",
" ... \n",
"329 10.777317\n",
"506 -11.415137\n",
"980 9.526360\n",
"678 13.061989\n",
"175 17.721185\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 491 True\n",
"155 True\n",
"644 True\n",
"918 True\n",
"979 True\n",
" ... \n",
"329 True\n",
"506 False\n",
"980 True\n",
"678 True\n",
"175 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Treatment): 0.1664\n",
"INFO:causalml: Gini (Control): 0.7113\n",
"INFO:causalml: Gini (Treatment): 0.9892\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0798\n",
"INFO:causalml: RMSE (Treatment): 0.9780\n",
"INFO:causalml: sMAPE (Control): 0.5958\n",
"INFO:causalml: sMAPE (Treatment): 0.1848\n",
"INFO:causalml: Gini (Control): 0.6842\n",
"INFO:causalml: Gini (Treatment): 0.9876\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"189 0.410161 0.131611 1.306199 -0.669237 -3.213176 -0.015185 1.042416 \n",
"137 -1.888267 0.840921 -1.225961 0.008345 -1.717243 -0.430938 -1.004454 \n",
"578 -0.187148 -0.657366 -0.529002 -0.708784 -0.369343 -0.096796 1.079143 \n",
"600 -1.989738 1.197631 -0.709695 0.095892 -1.323674 -0.340022 -0.786717 \n",
"882 -2.141692 0.421597 -1.563731 -0.476695 0.927827 -1.031005 0.219039 \n",
".. ... ... ... ... ... ... ... \n",
"241 -0.696679 2.374122 -0.824048 1.787343 0.229680 -2.830965 -0.440137 \n",
"217 -1.125068 0.957498 0.151769 0.436910 -1.737025 -1.745240 -0.371585 \n",
"580 -0.140849 2.105249 -0.247492 0.146588 -0.125673 -2.231402 -0.241249 \n",
"321 -2.075528 0.044587 0.680838 0.326194 -0.586312 -0.547235 0.670399 \n",
"545 -1.029716 2.032547 0.704595 1.014036 0.594667 0.421423 0.768095 \n",
"\n",
" X0 X1 \n",
"189 1.042416 -0.015185 \n",
"137 -1.004454 -0.430938 \n",
"578 1.079143 -0.096796 \n",
"600 -0.786717 -0.340022 \n",
"882 0.219039 -1.031005 \n",
".. ... ... \n",
"241 -0.440137 -2.830965 \n",
"217 -0.371585 -1.745240 \n",
"580 -0.241249 -2.231402 \n",
"321 0.670399 -0.547235 \n",
"545 0.768095 0.421423 \n",
"\n",
"[1000 rows x 9 columns], 'y': 189 5.702358\n",
"137 -9.707122\n",
"578 -2.946371\n",
"600 -2.038553\n",
"882 -4.513230\n",
" ... \n",
"241 7.793042\n",
"217 -0.259780\n",
"580 8.433110\n",
"321 -6.946700\n",
"545 15.065277\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 189 True\n",
"137 False\n",
"578 False\n",
"600 True\n",
"882 False\n",
" ... \n",
"241 True\n",
"217 True\n",
"580 True\n",
"321 False\n",
"545 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0771\n",
"INFO:causalml: RMSE (Treatment): 0.9774\n",
"INFO:causalml: sMAPE (Control): 0.5323\n",
"INFO:causalml: sMAPE (Treatment): 0.1741\n",
"INFO:causalml: Gini (Control): 0.7203\n",
"INFO:causalml: Gini (Treatment): 0.9870\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"143 -1.949957 1.517756 2.204676 1.303316 -0.217841 -1.651990 -0.515243 \n",
"196 -1.949786 1.115287 -0.853560 -0.572337 -1.419509 -1.086555 0.608502 \n",
"46 -0.888146 1.092166 -0.582271 0.300130 -1.650112 0.469455 1.900898 \n",
"9 -0.021802 1.130757 1.080258 -1.120494 -0.771512 0.783945 -0.188939 \n",
"17 -2.283447 0.963726 0.992729 0.659825 -0.523651 -1.637352 2.110831 \n",
".. ... ... ... ... ... ... ... \n",
"28 0.284972 -1.725446 -0.542758 -1.319756 -0.151833 -0.471153 -0.210616 \n",
"273 -0.692795 1.302004 -0.819690 1.466807 -1.801295 -0.348019 -2.020364 \n",
"162 -1.347888 1.001111 -0.895320 0.433071 1.873425 -2.217724 2.456335 \n",
"695 -0.281676 -0.435856 -0.023010 0.860228 -0.372742 -1.177590 0.391568 \n",
"794 -1.175251 -0.572507 -0.484807 0.213518 0.123263 -1.627262 0.296319 \n",
"\n",
" X0 X1 \n",
"143 -0.515243 -1.651990 \n",
"196 0.608502 -1.086555 \n",
"46 1.900898 0.469455 \n",
"9 -0.188939 0.783945 \n",
"17 2.110831 -1.637352 \n",
".. ... ... \n",
"28 -0.210616 -0.471153 \n",
"273 -2.020364 -0.348019 \n",
"162 2.456335 -2.217724 \n",
"695 0.391568 -1.177590 \n",
"794 0.296319 -1.627262 \n",
"\n",
"[1000 rows x 9 columns], 'y': 143 3.430961\n",
"196 1.676450\n",
"46 -6.116730\n",
"9 8.755592\n",
"17 10.402576\n",
" ... \n",
"28 4.753158\n",
"273 -2.115646\n",
"162 17.934588\n",
"695 8.766754\n",
"794 5.470791\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 143 True\n",
"196 True\n",
"46 False\n",
"9 True\n",
"17 True\n",
" ... \n",
"28 True\n",
"273 True\n",
"162 True\n",
"695 True\n",
"794 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"460 -0.475622 1.378347 0.597975 0.184503 -1.056560 -0.774878 0.997804 \n",
"270 -0.206312 0.198214 -0.002747 0.136514 -0.920272 -0.790116 -0.029807 \n",
"787 0.083772 2.897460 -1.819668 -1.691378 -0.779529 -0.571429 -0.318654 \n",
"363 -1.094109 1.596488 -0.209081 -0.635393 -0.348193 -2.724728 1.738500 \n",
"394 -1.193673 1.783805 -0.658297 0.524736 0.592888 0.449741 0.668359 \n",
".. ... ... ... ... ... ... ... \n",
"454 -0.374366 0.308604 -1.075751 0.502187 -1.780666 -0.147872 2.126364 \n",
"125 -1.513287 -1.100623 -1.587157 -0.180659 -0.209123 -1.245457 -0.609627 \n",
"441 0.191672 1.544473 -1.272017 -1.240143 -0.223964 0.151050 2.278019 \n",
"717 0.784248 1.796548 0.804206 -0.405701 -1.511015 -1.543735 -0.785670 \n",
"503 -2.011356 1.992276 -1.586584 0.628595 0.382689 -0.342653 1.223774 \n",
"\n",
" X0 X1 \n",
"460 0.997804 -0.774878 \n",
"270 -0.029807 -0.790116 \n",
"787 -0.318654 -0.571429 \n",
"363 1.738500 -2.724728 \n",
"394 0.668359 0.449741 \n",
".. ... ... \n",
"454 2.126364 -0.147872 \n",
"125 -0.609627 -1.245457 \n",
"441 2.278019 0.151050 \n",
"717 -0.785670 -1.543735 \n",
"503 1.223774 -0.342653 \n",
"\n",
"[1000 rows x 9 columns], 'y': 460 11.172108\n",
"270 6.749300\n",
"787 7.397373\n",
"363 8.937908\n",
"394 12.331256\n",
" ... \n",
"454 12.121477\n",
"125 -0.052585\n",
"441 18.248395\n",
"717 6.273750\n",
"503 -3.163402\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 460 True\n",
"270 True\n",
"787 True\n",
"363 True\n",
"394 True\n",
" ... \n",
"454 True\n",
"125 True\n",
"441 True\n",
"717 True\n",
"503 False\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1074\n",
"INFO:causalml: RMSE (Treatment): 1.0417\n",
"INFO:causalml: sMAPE (Control): 0.5225\n",
"INFO:causalml: sMAPE (Treatment): 0.1999\n",
"INFO:causalml: Gini (Control): 0.7209\n",
"INFO:causalml: Gini (Treatment): 0.9865\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0059\n",
"INFO:causalml: RMSE (Treatment): 1.0071\n",
"INFO:causalml: sMAPE (Control): 0.4864\n",
"INFO:causalml: sMAPE (Treatment): 0.1695\n",
"INFO:causalml: Gini (Control): 0.7898\n",
"INFO:causalml: Gini (Treatment): 0.9875\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9534\n",
"INFO:causalml: RMSE (Treatment): 1.0334\n",
"INFO:causalml: sMAPE (Control): 0.5827\n",
"INFO:causalml: sMAPE (Treatment): 0.1975\n",
"INFO:causalml: Gini (Control): 0.7350\n",
"INFO:causalml: Gini (Treatment): 0.9860\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"525 -1.257118 0.330664 -0.915888 0.671743 0.367080 -0.748137 0.450687 \n",
"240 -1.118451 0.973362 0.110315 1.164763 -0.021371 -0.742111 -0.164940 \n",
"35 -0.435681 -0.794540 -0.418219 -0.689760 0.350847 -1.512905 -0.001673 \n",
"466 0.020113 1.393934 -0.355963 1.711858 -0.590929 0.222977 1.567094 \n",
"243 -2.996842 -0.425847 0.431792 1.635614 -1.242695 -0.323142 1.143186 \n",
".. ... ... ... ... ... ... ... \n",
"795 -1.103395 1.605360 -1.577250 -0.179236 0.174861 0.283471 -0.625883 \n",
"979 -1.343553 -0.429367 -0.073222 1.017619 0.843141 -0.743642 0.564612 \n",
"446 0.592343 0.313503 -0.041499 1.056622 0.324638 0.685480 -2.317766 \n",
"663 0.921055 0.125452 0.450490 -0.032753 -0.320248 0.770046 0.389654 \n",
"245 -1.757050 1.779740 -0.756241 -0.346250 -1.023875 -0.470867 0.000910 \n",
"\n",
" X0 X1 \n",
"525 0.450687 -0.748137 \n",
"240 -0.164940 -0.742111 \n",
"35 -0.001673 -1.512905 \n",
"466 1.567094 0.222977 \n",
"243 1.143186 -0.323142 \n",
".. ... ... \n",
"795 -0.625883 0.283471 \n",
"979 0.564612 -0.743642 \n",
"446 -2.317766 0.685480 \n",
"663 0.389654 0.770046 \n",
"245 0.000910 -0.470867 \n",
"\n",
"[1000 rows x 9 columns], 'y': 525 9.406466\n",
"240 6.512254\n",
"35 -2.892738\n",
"466 18.638368\n",
"243 -10.483545\n",
" ... \n",
"795 5.028396\n",
"979 10.393456\n",
"446 6.907692\n",
"663 15.384533\n",
"245 2.675580\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 525 True\n",
"240 True\n",
"35 False\n",
"466 True\n",
"243 False\n",
" ... \n",
"795 True\n",
"979 True\n",
"446 True\n",
"663 True\n",
"245 True\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"206 0.103730 -1.222034 -0.933344 0.063977 1.399857 0.000319 2.151908 \n",
"560 -0.462661 -0.232212 -1.504443 -1.182863 -1.370777 0.188339 1.665963 \n",
"533 -1.862196 2.125772 -0.836537 0.032782 -2.380123 -1.400905 -1.115644 \n",
"528 -1.197186 2.033324 -0.122422 0.389914 -1.670525 0.477707 0.919813 \n",
"463 -1.450262 1.052490 -1.091478 0.585210 1.013792 0.704843 0.793687 \n",
".. ... ... ... ... ... ... ... \n",
"769 -1.792941 0.756132 -0.350476 -0.365354 -0.166349 0.270195 1.808962 \n",
"280 -0.952118 -0.646225 -1.024826 -0.408950 -0.476089 -1.776295 0.934463 \n",
"689 -2.206252 1.297100 -1.432744 -2.462582 -1.985402 -2.328632 -0.045690 \n",
"723 0.089334 1.225408 0.816162 0.782219 -0.602493 -0.468571 0.482690 \n",
"696 1.374044 1.843108 -0.542039 -0.043563 1.022823 -1.429075 1.809225 \n",
"\n",
" X0 X1 \n",
"206 2.151908 0.000319 \n",
"560 1.665963 0.188339 \n",
"533 -1.115644 -1.400905 \n",
"528 0.919813 0.477707 \n",
"463 0.793687 0.704843 \n",
".. ... ... \n",
"769 1.808962 0.270195 \n",
"280 0.934463 -1.776295 \n",
"689 -0.045690 -2.328632 \n",
"723 0.482690 -0.468571 \n",
"696 1.809225 -1.429075 \n",
"\n",
"[1000 rows x 9 columns], 'y': 206 20.680223\n",
"560 -7.215301\n",
"533 -6.739186\n",
"528 8.156158\n",
"463 13.719677\n",
" ... \n",
"769 -5.821681\n",
"280 5.994137\n",
"689 -13.994008\n",
"723 12.592456\n",
"696 23.158781\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 206 True\n",
"560 False\n",
"533 True\n",
"528 True\n",
"463 True\n",
" ... \n",
"769 False\n",
"280 True\n",
"689 False\n",
"723 True\n",
"696 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0897\n",
"INFO:causalml: RMSE (Treatment): 0.9911\n",
"INFO:causalml: sMAPE (Control): 0.5511\n",
"INFO:causalml: sMAPE (Treatment): 0.1703\n",
"INFO:causalml: Gini (Control): 0.7123\n",
"INFO:causalml: Gini (Treatment): 0.9874\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0492\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"952 -0.660953 0.410799 -1.691342 -0.427253 0.002600 -1.478529 1.918048 \n",
"877 -0.329610 1.058964 -0.095169 -1.850964 1.588369 1.510118 -0.178392 \n",
"895 -0.539152 -0.067219 -0.942383 0.206152 0.166660 0.376239 0.972146 \n",
"114 -0.246959 0.668501 -1.134474 0.249492 -1.934428 -1.135913 -0.308327 \n",
"457 -1.390795 1.570080 -0.639978 1.787026 -2.865565 -1.465397 -0.257055 \n",
".. ... ... ... ... ... ... ... \n",
"308 -1.176133 0.314924 0.418085 0.637007 0.911918 -0.742568 0.841509 \n",
"349 -1.713807 0.945541 -0.100213 0.359346 0.039680 -0.455719 1.268811 \n",
"764 -1.799079 2.439488 -0.823690 -0.679766 -0.140499 0.202744 1.818416 \n",
"873 -0.273349 -0.544929 0.632921 0.675695 -0.970007 -1.999815 0.067102 \n",
"991 -0.262942 0.695928 0.109928 -1.248148 -2.639630 -0.009511 1.550811 \n",
"\n",
" X0 X1 \n",
"952 1.918048 -1.478529 \n",
"877 -0.178392 1.510118 \n",
"895 0.972146 0.376239 \n",
"114 -0.308327 -1.135913 \n",
"457 -0.257055 -1.465397 \n",
".. ... ... \n",
"308 0.841509 -0.742568 \n",
"349 1.268811 -0.455719 \n",
"764 1.818416 0.202744 \n",
"873 0.067102 -1.999815 \n",
"991 1.550811 -0.009511 \n",
"\n",
"[1000 rows x 9 columns], 'y': 952 -2.868485\n",
"877 12.698322\n",
"895 12.243751\n",
"114 0.119504\n",
"457 -8.028104\n",
" ... \n",
"308 12.604981\n",
"349 10.315484\n",
"764 11.907958\n",
"873 4.499265\n",
"991 -7.648859\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 952 False\n",
"877 True\n",
"895 True\n",
"114 True\n",
"457 False\n",
" ... \n",
"308 True\n",
"349 True\n",
"764 True\n",
"873 True\n",
"991 False\n",
"Name: v0, Length: 1000, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"889 -0.164693 -0.098146 0.862596 0.255460 -0.216842 -1.656429 0.520855 \n",
"883 -1.149449 -0.077862 -0.130952 -0.114547 -1.504078 1.082394 0.597279 \n",
"495 -0.887332 0.992530 0.348676 0.724410 -0.731744 1.080723 -0.451440 \n",
"831 -1.405624 -0.589721 -0.969126 0.575371 -2.125845 -1.547868 1.996688 \n",
"987 -0.779654 1.379589 -0.474635 -0.208440 -0.895621 -0.919435 0.467998 \n",
".. ... ... ... ... ... ... ... \n",
"931 -0.593921 0.967747 -0.087519 -0.318961 -0.374575 -1.136714 0.905738 \n",
"930 0.300960 0.603222 -0.368445 -0.424044 0.242424 1.053366 -0.593627 \n",
"992 -0.972164 0.159489 -2.098758 0.192947 -0.229343 0.044889 -0.560626 \n",
"817 0.615579 1.267909 -0.445512 -0.048259 0.461022 -0.247928 1.070838 \n",
"647 -0.053906 1.500408 -0.769493 1.074275 -0.442873 1.186568 -0.897844 \n",
"\n",
" X0 X1 \n",
"889 0.520855 -1.656429 \n",
"883 0.597279 1.082394 \n",
"495 -0.451440 1.080723 \n",
"831 1.996688 -1.547868 \n",
"987 0.467998 -0.919435 \n",
".. ... ... \n",
"931 0.905738 -1.136714 \n",
"930 -0.593627 1.053366 \n",
"992 -0.560626 0.044889 \n",
"817 1.070838 -0.247928 \n",
"647 -0.897844 1.186568 \n",
"\n",
"[1000 rows x 9 columns], 'y': 889 9.124434\n",
"883 5.901248\n",
"495 7.428448\n",
"831 4.497600\n",
"987 -4.527449\n",
" ... \n",
"931 10.080089\n",
"930 10.114187\n",
"992 3.553867\n",
"817 17.203287\n",
"647 9.825764\n",
"Name: y, Length: 1000, dtype: float64, 'treatment': 889 True\n",
"883 True\n",
"495 True\n",
"831 True\n",
"987 False\n",
" ... \n",
"931 True\n",
"930 True\n",
"992 True\n",
"817 True\n",
"647 True\n",
"Name: v0, Length: 1000, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Treatment): 1.0259\n",
"INFO:causalml: sMAPE (Control): 0.4768\n",
"INFO:causalml: sMAPE (Treatment): 0.1909\n",
"INFO:causalml: Gini (Control): 0.6954\n",
"INFO:causalml: Gini (Treatment): 0.9849\n",
"INFO:dowhy.causal_refuters.bootstrap_refuter:Making use of Bootstrap as we have more than 100 examples.\n",
" Note: The greater the number of examples, the more accurate are the confidence estimates\n",
"INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.8 simulated datasets of size 800.0 each\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1266\n",
"INFO:causalml: RMSE (Treatment): 0.7583\n",
"INFO:causalml: sMAPE (Control): 0.5441\n",
"INFO:causalml: sMAPE (Treatment): 0.1523\n",
"INFO:causalml: Gini (Control): 0.7189\n",
"INFO:causalml: Gini (Treatment): 0.9944\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0595\n",
"INFO:causalml: RMSE (Treatment): 0.7478\n",
"INFO:causalml: sMAPE (Control): 0.5545\n",
"INFO:causalml: sMAPE (Treatment): 0.1526\n",
"INFO:causalml: Gini (Control): 0.7471\n",
"INFO:causalml: Gini (Treatment): 0.9948\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"526 1.675724 0.943376 0.067126 0.549606 0.792403 -0.578182 0.606835 \n",
"938 -1.315956 1.949162 -0.981438 -0.657011 1.021762 -1.702059 0.071287 \n",
"252 -0.407608 -0.441935 -1.985984 1.444090 0.083796 -0.670731 0.259822 \n",
"711 0.099864 0.069601 -0.148899 0.008564 -0.642494 -0.050277 0.035692 \n",
"946 -0.056748 1.082737 0.733627 1.643594 -0.787399 -1.668734 1.968422 \n",
".. ... ... ... ... ... ... ... \n",
"17 -2.362098 0.982769 0.900772 0.481628 -0.168328 -1.676616 2.100352 \n",
"789 -1.419550 -0.199775 1.439845 -0.043468 -0.070078 0.347234 1.237806 \n",
"970 -1.394364 1.786996 1.473136 -0.626023 -0.659760 -1.513833 -0.140064 \n",
"340 0.375616 2.217136 -0.666164 -0.970808 0.446217 -0.864206 1.763119 \n",
"150 -2.720297 0.875790 -0.364609 -0.201468 0.001957 -1.103898 0.006235 \n",
"\n",
" X0 X1 \n",
"526 0.606835 -0.578182 \n",
"938 0.071287 -1.702059 \n",
"252 0.259822 -0.670731 \n",
"711 0.035692 -0.050277 \n",
"946 1.968422 -1.668734 \n",
".. ... ... \n",
"17 2.100352 -1.676616 \n",
"789 1.237806 0.347234 \n",
"970 -0.140064 -1.513833 \n",
"340 1.763119 -0.864206 \n",
"150 0.006235 -1.103898 \n",
"\n",
"[800 rows x 9 columns], 'y': 526 20.291162\n",
"938 7.827260\n",
"252 -0.786820\n",
"711 8.687897\n",
"946 16.526801\n",
" ... \n",
"17 10.402576\n",
"789 11.103887\n",
"970 3.837965\n",
"340 18.529106\n",
"150 1.212438\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 526 True\n",
"938 True\n",
"252 False\n",
"711 True\n",
"946 True\n",
" ... \n",
"17 True\n",
"789 True\n",
"970 True\n",
"340 True\n",
"150 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"10 0.846619 2.980613 0.205696 2.969570 0.858587 -1.222764 1.147817 \n",
"753 -3.041559 0.539260 0.080762 -0.522629 -2.000581 -1.113098 0.679077 \n",
"832 -0.682884 1.926730 -0.161279 -0.512041 -0.170792 -1.586715 0.243828 \n",
"332 -1.250673 0.380127 -0.875099 1.332656 0.562342 0.345025 1.790799 \n",
"396 -0.251196 -1.935299 -1.143646 -1.311957 -1.503472 0.517977 0.501330 \n",
".. ... ... ... ... ... ... ... \n",
"837 -1.599117 0.022039 1.215839 0.749469 -1.528362 -1.775356 1.715229 \n",
"568 0.513430 0.522040 0.017360 -0.306262 -0.660055 -1.021097 1.297397 \n",
"925 -0.292250 -0.487899 -1.791678 1.743438 -1.451384 -1.451548 -0.034151 \n",
"594 -0.485088 0.198923 0.215890 1.387663 0.491391 -1.501354 -0.527038 \n",
"270 -0.167769 0.239291 -0.121369 0.249183 -0.697704 -0.932864 -0.077138 \n",
"\n",
" X0 X1 \n",
"10 1.147817 -1.222764 \n",
"753 0.679077 -1.113098 \n",
"832 0.243828 -1.586715 \n",
"332 1.790799 0.345025 \n",
"396 0.501330 0.517977 \n",
".. ... ... \n",
"837 1.715229 -1.775356 \n",
"568 1.297397 -1.021097 \n",
"925 -0.034151 -1.451548 \n",
"594 -0.527038 -1.501354 \n",
"270 -0.077138 -0.932864 \n",
"\n",
"[800 rows x 9 columns], 'y': 10 24.500212\n",
"753 -3.081806\n",
"832 7.911675\n",
"332 16.296656\n",
"396 -9.105144\n",
" ... \n",
"837 6.998667\n",
"568 13.469854\n",
"925 4.053751\n",
"594 8.167200\n",
"270 6.749300\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 10 True\n",
"753 True\n",
"832 True\n",
"332 True\n",
"396 False\n",
" ... \n",
"837 True\n",
"568 True\n",
"925 True\n",
"594 True\n",
"270 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0612\n",
"INFO:causalml: RMSE (Treatment): 0.6918\n",
"INFO:causalml: sMAPE (Control): 0.5598\n",
"INFO:causalml: sMAPE (Treatment): 0.1391\n",
"INFO:causalml: Gini (Control): 0.7077\n",
"INFO:causalml: Gini (Treatment): 0.9946\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"52 0.100104 1.279976 1.033007 1.442204 -1.120492 0.116882 -0.350810 \n",
"258 -1.776837 0.805692 -1.001388 0.520858 0.798608 -1.470144 0.534863 \n",
"631 0.271998 2.338601 -1.112341 1.567964 -0.208038 -0.964256 0.333245 \n",
"970 -1.394364 1.786996 1.473136 -0.626023 -0.659760 -1.513833 -0.140064 \n",
"499 -1.577195 -0.178122 0.207028 1.204303 -0.666172 1.145283 0.458618 \n",
".. ... ... ... ... ... ... ... \n",
"730 -0.464492 1.914988 0.441570 2.087402 0.778967 -0.037934 1.464653 \n",
"538 -1.000985 2.417859 0.027563 1.436945 1.891137 -2.054885 0.763508 \n",
"845 -1.215958 -0.485289 -1.167214 0.914403 -1.773236 0.103620 -0.518889 \n",
"979 -1.320009 -0.547319 0.058828 1.048133 1.078373 -0.857583 0.480911 \n",
"956 -0.371871 -2.250709 0.444837 1.333937 -1.775519 -1.056909 0.569966 \n",
"\n",
" X0 X1 \n",
"52 -0.350810 0.116882 \n",
"258 0.534863 -1.470144 \n",
"631 0.333245 -0.964256 \n",
"970 -0.140064 -1.513833 \n",
"499 0.458618 1.145283 \n",
".. ... ... \n",
"730 1.464653 -0.037934 \n",
"538 0.763508 -2.054885 \n",
"845 -0.518889 0.103620 \n",
"979 0.480911 -0.857583 \n",
"956 0.569966 -1.056909 \n",
"\n",
"[800 rows x 9 columns], 'y': 52 9.973921\n",
"258 8.016723\n",
"631 14.220450\n",
"970 3.837965\n",
"499 8.058945\n",
" ... \n",
"730 20.892188\n",
"538 16.761939\n",
"845 -8.309502\n",
"979 10.393456\n",
"956 4.438298\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 52 True\n",
"258 True\n",
"631 True\n",
"970 True\n",
"499 True\n",
" ... \n",
"730 True\n",
"538 True\n",
"845 False\n",
"979 True\n",
"956 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"139 -1.494898 1.076908 0.509008 -0.008836 0.810677 -0.868950 0.996169 \n",
"425 -0.547299 1.010457 0.355019 0.509403 -0.299352 -1.751637 1.623652 \n",
"512 -1.320082 1.599199 1.561314 2.979497 -1.486751 -1.162078 0.346264 \n",
"491 0.047326 0.391397 -0.404472 0.060057 0.642744 -0.800913 -0.652640 \n",
"441 0.096609 1.592914 -1.201277 -1.185385 -0.135004 0.210872 2.310466 \n",
".. ... ... ... ... ... ... ... \n",
"866 -2.492060 0.007731 -0.940262 -0.496057 1.104068 0.043952 0.520345 \n",
"838 -0.753922 -0.045816 -0.873461 1.047785 -0.824178 -1.028226 -0.543500 \n",
"942 -1.242170 1.302580 -0.669569 -0.436052 -0.873213 -1.051785 0.880964 \n",
"741 -0.491645 2.790552 -1.320505 -0.920479 -1.283653 -0.399134 -0.088422 \n",
"504 -1.822921 2.092858 0.863527 -0.530852 -1.148241 -0.820791 0.423434 \n",
"\n",
" X0 X1 \n",
"139 0.996169 -0.868950 \n",
"425 1.623652 -1.751637 \n",
"512 0.346264 -1.162078 \n",
"491 -0.652640 -0.800913 \n",
"441 2.310466 0.210872 \n",
".. ... ... \n",
"866 0.520345 0.043952 \n",
"838 -0.543500 -1.028226 \n",
"942 0.880964 -1.051785 \n",
"741 -0.088422 -0.399134 \n",
"504 0.423434 -0.820791 \n",
"\n",
"[800 rows x 9 columns], 'y': 139 11.778033\n",
"425 13.323890\n",
"512 8.367137\n",
"491 8.770425\n",
"441 18.248395\n",
" ... \n",
"866 6.414822\n",
"838 3.145267\n",
"942 6.530430\n",
"741 5.593102\n",
"504 4.307409\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 139 True\n",
"425 True\n",
"512 True\n",
"491 True\n",
"441 True\n",
" ... \n",
"866 True\n",
"838 True\n",
"942 True\n",
"741 True\n",
"504 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Control): 3.0428\n",
"INFO:causalml: RMSE (Treatment): 0.7573\n",
"INFO:causalml: sMAPE (Control): 0.5377\n",
"INFO:causalml: sMAPE (Treatment): 0.1472\n",
"INFO:causalml: Gini (Control): 0.7402\n",
"INFO:causalml: Gini (Treatment): 0.9938\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0320\n",
"INFO:causalml: RMSE (Treatment): 0.7062\n",
"INFO:causalml: sMAPE (Control): 0.5521\n",
"INFO:causalml: sMAPE (Treatment): 0.1479\n",
"INFO:causalml: Gini (Control): 0.7289\n",
"INFO:causalml: Gini (Treatment): 0.9946\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0591\n",
"INFO:causalml: RMSE (Treatment): 0.7414\n",
"INFO:causalml: sMAPE (Control): 0.5376\n",
"INFO:causalml: sMAPE (Treatment): 0.1489\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"556 -1.361029 0.354536 -0.575247 -1.264914 -0.170182 -2.098308 -0.219006 \n",
"591 -1.762898 1.105254 1.394248 0.278984 -0.431230 -0.144092 -0.668399 \n",
"603 0.768344 1.258159 -0.927093 0.093637 0.910444 -0.339078 1.272218 \n",
"65 -2.860295 0.491337 0.307032 0.402665 0.265118 -1.899636 -0.565288 \n",
"275 -2.120068 -0.318202 0.171676 1.909965 -0.392065 -1.480635 -1.148364 \n",
".. ... ... ... ... ... ... ... \n",
"238 -0.166010 0.774921 -0.462897 -0.294769 -1.389314 -0.210174 2.431329 \n",
"377 -2.792060 1.482096 -1.502509 -0.178419 -1.393264 -0.224015 -0.587890 \n",
"327 -1.263486 1.105278 -1.375127 0.828352 -2.622254 -1.555671 0.808030 \n",
"721 0.285260 -0.083247 -2.164773 0.446914 -0.582682 -1.433214 0.854157 \n",
"585 0.931665 0.595916 -0.103318 0.428966 0.099797 0.546834 0.794030 \n",
"\n",
" X0 X1 \n",
"556 -0.219006 -2.098308 \n",
"591 -0.668399 -0.144092 \n",
"603 1.272218 -0.339078 \n",
"65 -0.565288 -1.899636 \n",
"275 -1.148364 -1.480635 \n",
".. ... ... \n",
"238 2.431329 -0.210174 \n",
"377 -0.587890 -0.224015 \n",
"327 0.808030 -1.555671 \n",
"721 0.854157 -1.433214 \n",
"585 0.794030 0.546834 \n",
"\n",
"[800 rows x 9 columns], 'y': 556 0.746452\n",
"591 3.407731\n",
"603 19.861939\n",
"65 -0.860515\n",
"275 -1.163599\n",
" ... \n",
"238 14.670040\n",
"377 -3.680926\n",
"327 1.918859\n",
"721 9.936280\n",
"585 17.683899\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 556 True\n",
"591 True\n",
"603 True\n",
"65 True\n",
"275 True\n",
" ... \n",
"238 True\n",
"377 True\n",
"327 True\n",
"721 True\n",
"585 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"927 -0.585546 0.718989 -1.390052 1.452628 -1.072161 0.379900 1.646249 \n",
"501 -1.563067 0.827427 1.267829 -0.259303 -1.929615 -0.584444 0.461660 \n",
"25 -0.690786 1.699003 -0.408257 0.040733 -2.094357 -0.544787 1.407147 \n",
"50 -0.250699 -0.035041 -0.605283 0.316777 -1.389142 1.439943 0.671531 \n",
"858 -2.847642 1.463499 1.836013 -0.281452 -0.828618 -0.584263 -0.020862 \n",
".. ... ... ... ... ... ... ... \n",
"505 -2.479285 0.847818 0.513598 -0.218587 0.793758 -1.587561 1.340198 \n",
"478 -1.186330 1.319188 -0.988151 0.448109 0.338470 -1.917601 1.262970 \n",
"67 -0.786445 0.153501 -0.155530 1.019364 -0.536998 -2.323703 -1.580963 \n",
"773 -1.096836 -0.432951 -0.837524 -0.231672 -0.535628 -1.020083 -0.353180 \n",
"971 -1.011609 0.842579 -1.257641 1.094270 0.446977 1.836771 1.109406 \n",
"\n",
" X0 X1 \n",
"927 1.646249 0.379900 \n",
"501 0.461660 -0.584444 \n",
"25 1.407147 -0.544787 \n",
"50 0.671531 1.439943 \n",
"858 -0.020862 -0.584263 \n",
".. ... ... \n",
"505 1.340198 -1.587561 \n",
"478 1.262970 -1.917601 \n",
"67 -1.580963 -2.323703 \n",
"773 -0.353180 -1.020083 \n",
"971 1.109406 1.836771 \n",
"\n",
"[800 rows x 9 columns], 'y': 927 -2.729883\n",
"501 2.593155\n",
"25 8.623876\n",
"50 9.813211\n",
"858 0.976517\n",
" ... \n",
"505 8.711479\n",
"478 11.134846\n",
"67 -0.972001\n",
"773 1.630931\n",
"971 16.046613\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 927 False\n",
"501 True\n",
"25 True\n",
"50 True\n",
"858 True\n",
" ... \n",
"505 True\n",
"478 True\n",
"67 True\n",
"773 True\n",
"971 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Control): 0.7470\n",
"INFO:causalml: Gini (Treatment): 0.9945\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0006\n",
"INFO:causalml: RMSE (Treatment): 0.6701\n",
"INFO:causalml: sMAPE (Control): 0.5528\n",
"INFO:causalml: sMAPE (Treatment): 0.1413\n",
"INFO:causalml: Gini (Control): 0.7535\n",
"INFO:causalml: Gini (Treatment): 0.9958\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9126\n",
"INFO:causalml: RMSE (Treatment): 0.7190\n",
"INFO:causalml: sMAPE (Control): 0.5292\n",
"INFO:causalml: sMAPE (Treatment): 0.1424\n",
"INFO:causalml: Gini (Control): 0.7453\n",
"INFO:causalml: Gini (Treatment): 0.9944\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"189 0.449357 0.147778 1.294543 -0.691706 -3.244261 -0.423599 0.935502 \n",
"280 -0.924446 -0.495998 -1.067446 -0.475160 -0.560881 -1.593906 1.057462 \n",
"516 -0.316795 2.424823 0.020510 -0.536910 0.983380 -0.880608 1.127091 \n",
"343 -1.245795 1.748387 0.828184 -1.775403 -0.972468 -0.866709 0.489782 \n",
"554 -1.733641 -1.598375 1.607236 0.161280 -1.096390 -2.406156 -0.029761 \n",
".. ... ... ... ... ... ... ... \n",
"588 -0.930255 -0.378142 0.632255 0.279449 -1.818598 -1.226886 0.208515 \n",
"517 -1.392397 0.066449 -0.113032 1.352150 -0.975298 -0.875759 1.033629 \n",
"544 -1.005914 0.255475 -1.627301 1.838873 0.013614 -1.474838 1.050279 \n",
"622 -0.848690 -0.845794 0.267025 -0.137135 0.356153 -0.001756 0.376872 \n",
"808 -0.291951 1.043771 -0.571882 -0.537580 0.895934 -1.203433 1.200730 \n",
"\n",
" X0 X1 \n",
"189 0.935502 -0.423599 \n",
"280 1.057462 -1.593906 \n",
"516 1.127091 -0.880608 \n",
"343 0.489782 -0.866709 \n",
"554 -0.029761 -2.406156 \n",
".. ... ... \n",
"588 0.208515 -1.226886 \n",
"517 1.033629 -0.875759 \n",
"544 1.050279 -1.474838 \n",
"622 0.376872 -0.001756 \n",
"808 1.200730 -1.203433 \n",
"\n",
"[800 rows x 9 columns], 'y': 189 5.702358\n",
"280 5.994137\n",
"516 16.717229\n",
"343 4.731928\n",
"554 -8.661899\n",
" ... \n",
"588 -7.223205\n",
"517 -4.981074\n",
"544 10.741956\n",
"622 8.845133\n",
"808 14.588358\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 189 True\n",
"280 True\n",
"516 True\n",
"343 True\n",
"554 False\n",
" ... \n",
"588 False\n",
"517 False\n",
"544 True\n",
"622 True\n",
"808 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"639 -2.186421 -0.027222 -0.566212 0.233679 -0.689588 0.053065 1.482137 \n",
"95 -0.058838 4.762058 0.823520 0.244461 -0.340888 -2.698332 -0.572900 \n",
"310 -1.369793 1.308092 0.005217 0.374738 -0.826930 -1.722449 -1.553554 \n",
"852 -3.069755 1.000289 -1.401000 0.920461 0.886048 -1.676077 0.615709 \n",
"82 -1.435084 0.702458 -0.750261 1.113334 -0.917545 -0.649801 -1.010522 \n",
".. ... ... ... ... ... ... ... \n",
"413 -0.344967 1.226283 -2.856753 -0.996308 -3.040920 -0.789789 0.101225 \n",
"620 -0.173878 -0.822082 0.608737 -0.477850 -0.325762 -0.578554 1.573905 \n",
"337 -2.268327 0.960651 -1.943224 -0.899046 -0.893776 -0.366596 -0.470081 \n",
"916 -2.328647 0.329460 -0.298049 0.392304 0.526570 0.266795 -0.277838 \n",
"741 -0.491645 2.790552 -1.320505 -0.920479 -1.283653 -0.399134 -0.088422 \n",
"\n",
" X0 X1 \n",
"639 1.482137 0.053065 \n",
"95 -0.572900 -2.698332 \n",
"310 -1.553554 -1.722449 \n",
"852 0.615709 -1.676077 \n",
"82 -1.010522 -0.649801 \n",
".. ... ... \n",
"413 0.101225 -0.789789 \n",
"620 1.573905 -0.578554 \n",
"337 -0.470081 -0.366596 \n",
"916 -0.277838 0.266795 \n",
"741 -0.088422 -0.399134 \n",
"\n",
"[800 rows x 9 columns], 'y': 639 7.178158\n",
"95 9.600613\n",
"310 -2.023777\n",
"852 4.981610\n",
"82 0.633027\n",
" ... \n",
"413 -10.466301\n",
"620 12.522042\n",
"337 -10.149884\n",
"916 4.529654\n",
"741 5.593102\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 639 True\n",
"95 True\n",
"310 True\n",
"852 True\n",
"82 True\n",
" ... \n",
"413 False\n",
"620 True\n",
"337 False\n",
"916 True\n",
"741 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9552\n",
"INFO:causalml: RMSE (Treatment): 0.6090\n",
"INFO:causalml: sMAPE (Control): 0.5162\n",
"INFO:causalml: sMAPE (Treatment): 0.1294\n",
"INFO:causalml: Gini (Control): 0.7544\n",
"INFO:causalml: Gini (Treatment): 0.9965\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1323\n",
"INFO:causalml: RMSE (Treatment): 0.7235\n",
"INFO:causalml: sMAPE (Control): 0.5527\n",
"INFO:causalml: sMAPE (Treatment): 0.1525\n",
"INFO:causalml: Gini (Control): 0.7191\n",
"INFO:causalml: Gini (Treatment): 0.9951\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"410 -0.703120 0.450567 -0.504908 0.896033 -1.220663 -3.269095 -1.609582 \n",
"85 -1.598213 1.517630 -0.586852 -0.114137 -0.935007 -2.850025 -0.268890 \n",
"207 -1.340535 1.069060 1.086892 0.379268 1.663276 -2.166838 0.824418 \n",
"286 -0.767221 1.168926 0.081677 0.486259 -1.291802 0.482044 0.276275 \n",
"895 -0.459769 -0.017087 -0.860174 0.294297 0.061199 0.406711 0.845962 \n",
".. ... ... ... ... ... ... ... \n",
"705 0.261741 3.188563 -2.010226 -0.517940 1.029141 0.040541 0.750995 \n",
"529 0.614057 -0.173724 0.014769 -0.498167 -2.710425 -1.591953 0.364769 \n",
"385 0.098676 -0.311522 -0.271842 0.307693 -1.909032 -1.327209 -1.324019 \n",
"957 -0.096493 2.758566 -0.130268 0.723499 -1.958197 0.441628 -0.668551 \n",
"434 -1.319228 1.447448 0.649329 -0.209742 -0.678209 -2.074434 -0.466893 \n",
"\n",
" X0 X1 \n",
"410 -1.609582 -3.269095 \n",
"85 -0.268890 -2.850025 \n",
"207 0.824418 -2.166838 \n",
"286 0.276275 0.482044 \n",
"895 0.845962 0.406711 \n",
".. ... ... \n",
"705 0.750995 0.040541 \n",
"529 0.364769 -1.591953 \n",
"385 -1.324019 -1.327209 \n",
"957 -0.668551 0.441628 \n",
"434 -0.466893 -2.074434 \n",
"\n",
"[800 rows x 9 columns], 'y': 410 -3.800252\n",
"85 -0.384362\n",
"207 13.068250\n",
"286 7.882269\n",
"895 12.243751\n",
" ... \n",
"705 18.039614\n",
"529 3.288937\n",
"385 -1.265529\n",
"957 6.531621\n",
"434 1.849930\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 410 True\n",
"85 True\n",
"207 True\n",
"286 True\n",
"895 True\n",
" ... \n",
"705 True\n",
"529 True\n",
"385 True\n",
"957 True\n",
"434 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"406 -1.787177 0.601264 -0.241539 -0.288932 0.204519 1.070860 0.778961 \n",
"98 -0.724707 -0.167508 -2.200046 1.316365 -0.652669 1.877820 0.372580 \n",
"118 -1.274995 1.158275 0.289024 -0.612569 -0.654518 0.424480 0.256109 \n",
"581 -0.357955 2.656209 -1.192847 -0.701790 -1.368029 -1.040599 1.376946 \n",
"792 -0.720148 0.309347 -0.138121 -1.657897 -0.246685 -0.155534 1.208282 \n",
".. ... ... ... ... ... ... ... \n",
"913 -1.875782 0.226743 -0.219598 2.054710 0.145892 -0.134735 0.897215 \n",
"141 -0.778642 1.177801 -1.433150 0.422531 1.560049 -1.618745 1.400487 \n",
"892 -1.218494 0.819447 -0.725373 -0.563789 -1.077169 -0.844040 -0.904803 \n",
"749 -0.836545 0.213895 1.151435 1.882737 -0.742327 -1.583142 1.220717 \n",
"32 -0.949214 0.797576 0.148739 -0.683603 -1.245559 0.625313 0.634566 \n",
"\n",
" X0 X1 \n",
"406 0.778961 1.070860 \n",
"98 0.372580 1.877820 \n",
"118 0.256109 0.424480 \n",
"581 1.376946 -1.040599 \n",
"792 1.208282 -0.155534 \n",
".. ... ... \n",
"913 0.897215 -0.134735 \n",
"141 1.400487 -1.618745 \n",
"892 -0.904803 -0.844040 \n",
"749 1.220717 -1.583142 \n",
"32 0.634566 0.625313 \n",
"\n",
"[800 rows x 9 columns], 'y': 406 9.585905\n",
"98 -3.658929\n",
"118 6.682644\n",
"581 10.512753\n",
"792 9.683267\n",
" ... \n",
"913 10.638397\n",
"141 15.976815\n",
"892 -0.915984\n",
"749 11.307324\n",
"32 -5.941016\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 406 True\n",
"98 False\n",
"118 True\n",
"581 True\n",
"792 True\n",
" ... \n",
"913 True\n",
"141 True\n",
"892 True\n",
"749 True\n",
"32 False\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0749\n",
"INFO:causalml: RMSE (Treatment): 0.7530\n",
"INFO:causalml: sMAPE (Control): 0.5563\n",
"INFO:causalml: sMAPE (Treatment): 0.1509\n",
"INFO:causalml: Gini (Control): 0.7384\n",
"INFO:causalml: Gini (Treatment): 0.9941\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0160\n",
"INFO:causalml: RMSE (Treatment): 0.7073\n",
"INFO:causalml: sMAPE (Control): 0.5457\n",
"INFO:causalml: sMAPE (Treatment): 0.1420\n",
"INFO:causalml: Gini (Control): 0.7585\n",
"INFO:causalml: Gini (Treatment): 0.9950\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"909 -0.448326 1.299826 0.401639 -0.500486 -1.006631 -0.906303 1.442992 \n",
"349 -1.755474 1.024933 -0.194372 0.339537 0.120379 -0.514354 1.189622 \n",
"814 0.459184 2.326171 -0.114560 0.137966 -0.677452 -1.446683 -0.415932 \n",
"808 -0.291951 1.043771 -0.571882 -0.537580 0.895934 -1.203433 1.200730 \n",
"316 -0.460513 0.381744 -1.155697 -0.412682 0.696926 -0.399444 0.467115 \n",
".. ... ... ... ... ... ... ... \n",
"175 -1.154340 0.806588 -0.662689 0.887440 0.111731 -1.009443 2.862545 \n",
"586 -1.822525 1.328268 -0.580694 0.434084 -1.937340 0.184012 3.008666 \n",
"339 -0.174635 1.102320 -0.538718 1.353400 -2.411045 -0.088519 -0.223676 \n",
"885 -2.040345 -0.486322 -1.615560 0.418978 -1.620201 0.405398 0.986656 \n",
"775 -2.360643 1.573135 -1.344099 0.823569 -1.137799 -0.227823 0.357754 \n",
"\n",
" X0 X1 \n",
"909 1.442992 -0.906303 \n",
"349 1.189622 -0.514354 \n",
"814 -0.415932 -1.446683 \n",
"808 1.200730 -1.203433 \n",
"316 0.467115 -0.399444 \n",
".. ... ... \n",
"175 2.862545 -1.009443 \n",
"586 3.008666 0.184012 \n",
"339 -0.223676 -0.088519 \n",
"885 0.986656 0.405398 \n",
"775 0.357754 -0.227823 \n",
"\n",
"[800 rows x 9 columns], 'y': 909 11.235374\n",
"349 10.315484\n",
"814 8.970856\n",
"808 14.588358\n",
"316 10.974035\n",
" ... \n",
"175 17.721185\n",
"586 -8.767996\n",
"339 4.815990\n",
"885 -11.199742\n",
"775 -7.934090\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 909 True\n",
"349 True\n",
"814 True\n",
"808 True\n",
"316 True\n",
" ... \n",
"175 True\n",
"586 False\n",
"339 True\n",
"885 False\n",
"775 False\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"680 -2.086015 0.221983 0.305686 -0.239145 0.557483 -2.016304 -0.558481 \n",
"479 -2.501195 0.440413 -0.336028 -0.086759 -2.148200 0.364346 -0.500635 \n",
"127 -1.184442 1.633850 -0.489806 0.806896 -0.654125 -0.324203 -1.297638 \n",
"696 1.393340 1.758799 -0.511376 -0.081397 0.909083 -1.352712 1.852517 \n",
"391 -0.307159 1.106719 1.338069 -1.242240 0.718523 0.202760 0.500101 \n",
".. ... ... ... ... ... ... ... \n",
"85 -1.598213 1.517630 -0.586852 -0.114137 -0.935007 -2.850025 -0.268890 \n",
"944 -1.357226 1.319787 0.307083 -0.175397 0.241389 -2.086970 0.097158 \n",
"677 -0.926078 0.166307 -2.183101 1.988610 -0.286812 -1.952046 0.131363 \n",
"71 -0.217006 0.453172 -1.028966 1.065666 0.093415 0.336570 1.457445 \n",
"877 -0.359565 0.826991 -0.179359 -1.811258 1.418593 1.405163 -0.075609 \n",
"\n",
" X0 X1 \n",
"680 -0.558481 -2.016304 \n",
"479 -0.500635 0.364346 \n",
"127 -1.297638 -0.324203 \n",
"696 1.852517 -1.352712 \n",
"391 0.500101 0.202760 \n",
".. ... ... \n",
"85 -0.268890 -2.850025 \n",
"944 0.097158 -2.086970 \n",
"677 0.131363 -1.952046 \n",
"71 1.457445 0.336570 \n",
"877 -0.075609 1.405163 \n",
"\n",
"[800 rows x 9 columns], 'y': 680 -4.551836\n",
"479 -4.199785\n",
"127 2.218793\n",
"696 23.158781\n",
"391 13.502571\n",
" ... \n",
"85 -0.384362\n",
"944 5.893961\n",
"677 6.044637\n",
"71 16.548460\n",
"877 12.698322\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 680 False\n",
"479 True\n",
"127 True\n",
"696 True\n",
"391 True\n",
" ... \n",
"85 True\n",
"944 True\n",
"677 True\n",
"71 True\n",
"877 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0539\n",
"INFO:causalml: RMSE (Treatment): 0.7237\n",
"INFO:causalml: sMAPE (Control): 0.5291\n",
"INFO:causalml: sMAPE (Treatment): 0.1414\n",
"INFO:causalml: Gini (Control): 0.7237\n",
"INFO:causalml: Gini (Treatment): 0.9946\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0446\n",
"INFO:causalml: RMSE (Treatment): 0.7516\n",
"INFO:causalml: sMAPE (Control): 0.5363\n",
"INFO:causalml: sMAPE (Treatment): 0.1535\n",
"INFO:causalml: Gini (Control): 0.7290\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"703 -0.880997 -1.820627 0.171736 -0.167854 0.599802 -2.011412 -0.082251 \n",
"839 -0.294596 0.983148 -0.666509 0.796590 0.072181 -0.815520 0.188710 \n",
"893 -1.491824 0.452183 -1.205184 0.340149 1.739224 -0.632809 0.210046 \n",
"187 0.166212 1.756207 0.174379 0.739862 -0.309621 0.009743 -0.960418 \n",
"578 -0.125015 -0.484719 -0.554158 -0.644919 -0.352951 -0.169403 0.807828 \n",
".. ... ... ... ... ... ... ... \n",
"230 -1.551489 1.387095 -0.588754 0.241508 -1.112741 -0.355862 0.419304 \n",
"541 0.055686 -0.430488 -0.206977 -1.310188 -0.050773 -0.528455 -0.335622 \n",
"222 -1.038459 1.255839 0.229480 -2.465858 -0.747697 -0.352680 1.793597 \n",
"352 -0.314322 -0.439802 2.366604 0.701073 -0.609823 0.603549 -0.256803 \n",
"8 -1.192098 0.543069 0.448059 1.496816 1.148883 -0.101334 0.352319 \n",
"\n",
" X0 X1 \n",
"703 -0.082251 -2.011412 \n",
"839 0.188710 -0.815520 \n",
"893 0.210046 -0.632809 \n",
"187 -0.960418 0.009743 \n",
"578 0.807828 -0.169403 \n",
".. ... ... \n",
"230 0.419304 -0.355862 \n",
"541 -0.335622 -0.528455 \n",
"222 1.793597 -0.352680 \n",
"352 -0.256803 0.603549 \n",
"8 0.352319 -0.101334 \n",
"\n",
"[800 rows x 9 columns], 'y': 703 -3.147667\n",
"839 10.718157\n",
"893 10.444157\n",
"187 9.138120\n",
"578 -2.946371\n",
" ... \n",
"230 5.106041\n",
"541 5.970889\n",
"222 9.575547\n",
"352 8.970110\n",
"8 13.374297\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 703 False\n",
"839 True\n",
"893 True\n",
"187 True\n",
"578 False\n",
" ... \n",
"230 True\n",
"541 True\n",
"222 True\n",
"352 True\n",
"8 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"444 -0.480592 0.778385 -2.187205 0.209081 -0.399762 -1.417665 0.987812 \n",
"436 -0.829198 -0.708516 -0.845920 0.281235 0.360107 -0.575813 0.086546 \n",
"873 -0.180674 -0.491029 0.704617 0.672175 -1.146367 -2.164384 -0.005079 \n",
"880 -1.838863 1.807867 2.274113 -0.514016 0.766872 -0.627074 0.533003 \n",
"336 -3.093855 1.469971 0.367279 1.174817 -0.729766 -2.114001 0.885400 \n",
".. ... ... ... ... ... ... ... \n",
"377 -2.792060 1.482096 -1.502509 -0.178419 -1.393264 -0.224015 -0.587890 \n",
"625 -1.336550 0.261883 0.557248 -0.069210 1.161863 -0.761143 1.075224 \n",
"810 -0.497451 0.532572 0.048320 0.565230 -0.286114 -0.078723 -0.839620 \n",
"403 -1.630763 2.405041 0.712370 0.529186 -2.230960 -0.416914 -1.593093 \n",
"229 -1.660012 1.065827 0.301958 1.601869 0.377531 0.371159 3.128694 \n",
"\n",
" X0 X1 \n",
"444 0.987812 -1.417665 \n",
"436 0.086546 -0.575813 \n",
"873 -0.005079 -2.164384 \n",
"880 0.533003 -0.627074 \n",
"336 0.885400 -2.114001 \n",
".. ... ... \n",
"377 -0.587890 -0.224015 \n",
"625 1.075224 -0.761143 \n",
"810 -0.839620 -0.078723 \n",
"403 -1.593093 -0.416914 \n",
"229 3.128694 0.371159 \n",
"\n",
"[800 rows x 9 columns], 'y': 444 9.305666\n",
"436 -2.400060\n",
"873 4.499265\n",
"880 10.445032\n",
"336 2.926169\n",
" ... \n",
"377 -3.680926\n",
"625 12.619755\n",
"810 5.956647\n",
"403 -3.265557\n",
"229 21.267379\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 444 True\n",
"436 False\n",
"873 True\n",
"880 True\n",
"336 True\n",
" ... \n",
"377 True\n",
"625 True\n",
"810 True\n",
"403 True\n",
"229 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Treatment): 0.9940\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0310\n",
"INFO:causalml: RMSE (Treatment): 0.6508\n",
"INFO:causalml: sMAPE (Control): 0.5466\n",
"INFO:causalml: sMAPE (Treatment): 0.1352\n",
"INFO:causalml: Gini (Control): 0.7524\n",
"INFO:causalml: Gini (Treatment): 0.9958\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0423\n",
"INFO:causalml: RMSE (Treatment): 0.6303\n",
"INFO:causalml: sMAPE (Control): 0.5129\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"749 -0.836545 0.213895 1.151435 1.882737 -0.742327 -1.583142 1.220717 \n",
"972 0.023127 0.650561 -0.921566 1.750151 1.136720 -1.237667 -0.986899 \n",
"268 -0.008387 -0.345640 0.297517 0.932659 -0.233741 -0.957095 -1.559348 \n",
"456 -1.180741 1.678642 0.789419 1.087730 0.037308 0.563234 0.500563 \n",
"444 -0.480592 0.778385 -2.187205 0.209081 -0.399762 -1.417665 0.987812 \n",
".. ... ... ... ... ... ... ... \n",
"798 0.298268 1.287738 -0.339625 0.484676 -1.361646 -0.844488 0.584065 \n",
"119 -0.412019 -1.707939 0.133539 0.898397 -0.839980 -0.812084 1.197569 \n",
"891 -0.294883 -0.574886 -1.010901 1.587116 0.836384 0.457966 2.071495 \n",
"649 -2.154564 1.280369 -0.834181 1.662134 -0.490512 0.051132 1.668837 \n",
"928 0.109927 -0.944171 -0.797629 0.166504 -0.799982 0.651497 -1.165977 \n",
"\n",
" X0 X1 \n",
"749 1.220717 -1.583142 \n",
"972 -0.986899 -1.237667 \n",
"268 -1.559348 -0.957095 \n",
"456 0.500563 0.563234 \n",
"444 0.987812 -1.417665 \n",
".. ... ... \n",
"798 0.584065 -0.844488 \n",
"119 1.197569 -0.812084 \n",
"891 2.071495 0.457966 \n",
"649 1.668837 0.051132 \n",
"928 -1.165977 0.651497 \n",
"\n",
"[800 rows x 9 columns], 'y': 749 11.307324\n",
"972 10.333966\n",
"268 3.465358\n",
"456 12.752919\n",
"444 9.305666\n",
" ... \n",
"798 10.206839\n",
"119 9.223485\n",
"891 20.166272\n",
"649 11.515556\n",
"928 3.466874\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 749 True\n",
"972 True\n",
"268 True\n",
"456 True\n",
"444 True\n",
" ... \n",
"798 True\n",
"119 True\n",
"891 True\n",
"649 True\n",
"928 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"267 -0.878236 2.441374 0.562945 0.761842 -1.538557 -0.622399 0.837778 \n",
"261 -1.476037 1.628997 -0.398342 1.161776 0.247181 0.421408 -0.968428 \n",
"816 -0.262987 0.921280 -0.389069 -0.883456 -2.158824 -1.767588 -0.312548 \n",
"973 0.403785 0.820598 -1.545243 1.044494 1.061604 -1.578398 -1.347821 \n",
"172 -1.078399 1.722034 -1.675423 -0.026819 -0.577141 -1.633102 1.619526 \n",
".. ... ... ... ... ... ... ... \n",
"581 -0.357955 2.656209 -1.192847 -0.701790 -1.368029 -1.040599 1.376946 \n",
"947 -1.534655 -0.090160 -0.309462 -0.872775 -0.925303 -0.554704 1.455779 \n",
"349 -1.755474 1.024933 -0.194372 0.339537 0.120379 -0.514354 1.189622 \n",
"791 -0.302223 -0.035221 -0.779010 -1.067815 -1.088018 1.298864 1.837893 \n",
"457 -1.376599 1.461089 -0.718660 1.781923 -2.802867 -1.477809 0.128276 \n",
"\n",
" X0 X1 \n",
"267 0.837778 -0.622399 \n",
"261 -0.968428 0.421408 \n",
"816 -0.312548 -1.767588 \n",
"973 -1.347821 -1.578398 \n",
"172 1.619526 -1.633102 \n",
".. ... ... \n",
"581 1.376946 -1.040599 \n",
"947 1.455779 -0.554704 \n",
"349 1.189622 -0.514354 \n",
"791 1.837893 1.298864 \n",
"457 0.128276 -1.477809 \n",
"\n",
"[800 rows x 9 columns], 'y': 267 9.609806\n",
"261 6.314564\n",
"816 -0.020121\n",
"973 8.500263\n",
"172 10.160763\n",
" ... \n",
"581 10.512753\n",
"947 6.326875\n",
"349 10.315484\n",
"791 12.700214\n",
"457 -8.028104\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 267 True\n",
"261 True\n",
"816 True\n",
"973 True\n",
"172 True\n",
" ... \n",
"581 True\n",
"947 True\n",
"349 True\n",
"791 True\n",
"457 False\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Treatment): 0.1280\n",
"INFO:causalml: Gini (Control): 0.7234\n",
"INFO:causalml: Gini (Treatment): 0.9959\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9207\n",
"INFO:causalml: RMSE (Treatment): 0.7112\n",
"INFO:causalml: sMAPE (Control): 0.5407\n",
"INFO:causalml: sMAPE (Treatment): 0.1472\n",
"INFO:causalml: Gini (Control): 0.7708\n",
"INFO:causalml: Gini (Treatment): 0.9953\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0262\n",
"INFO:causalml: RMSE (Treatment): 0.6868\n",
"INFO:causalml: sMAPE (Control): 0.5434\n",
"INFO:causalml: sMAPE (Treatment): 0.1467\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"418 -1.575082 -0.671851 -0.031660 -0.315301 0.096593 0.101204 -0.833223 \n",
"113 0.062439 3.227941 -0.979419 0.293534 -0.847729 0.604221 -0.504114 \n",
"60 0.305279 -0.220252 -0.191311 1.984745 -1.325471 0.975962 -1.100874 \n",
"135 -1.650996 2.260611 -1.131024 1.404627 -3.453988 0.098573 0.729032 \n",
"301 -0.802656 -0.970350 0.487567 0.756892 -0.372002 -0.915336 1.021589 \n",
".. ... ... ... ... ... ... ... \n",
"835 0.331235 0.078864 -0.421835 -0.926551 1.775880 0.278762 1.594422 \n",
"8 -1.192098 0.543069 0.448059 1.496816 1.148883 -0.101334 0.352319 \n",
"37 -0.057527 1.104099 -1.337860 0.103293 1.118403 -1.084497 0.679618 \n",
"49 -0.982952 2.225363 -1.526931 -0.694241 0.029243 -0.455032 0.234971 \n",
"939 -0.909925 1.045910 0.159272 -0.074724 -0.073039 -1.209146 2.115223 \n",
"\n",
" X0 X1 \n",
"418 -0.833223 0.101204 \n",
"113 -0.504114 0.604221 \n",
"60 -1.100874 0.975962 \n",
"135 0.729032 0.098573 \n",
"301 1.021589 -0.915336 \n",
".. ... ... \n",
"835 1.594422 0.278762 \n",
"8 0.352319 -0.101334 \n",
"37 0.679618 -1.084497 \n",
"49 0.234971 -0.455032 \n",
"939 2.115223 -1.209146 \n",
"\n",
"[800 rows x 9 columns], 'y': 418 -5.534462\n",
"113 10.245959\n",
"60 6.681242\n",
"135 2.418843\n",
"301 9.391349\n",
" ... \n",
"835 20.490073\n",
"8 13.374297\n",
"37 14.525794\n",
"49 8.274390\n",
"939 14.519592\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 418 False\n",
"113 True\n",
"60 True\n",
"135 True\n",
"301 True\n",
" ... \n",
"835 True\n",
"8 True\n",
"37 True\n",
"49 True\n",
"939 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"463 -1.384868 1.213614 -1.227984 0.542554 0.851906 1.016157 0.823336 \n",
"425 -0.547299 1.010457 0.355019 0.509403 -0.299352 -1.751637 1.623652 \n",
"615 -1.312910 0.549883 -0.743413 1.273369 -2.435562 -1.071918 2.090585 \n",
"842 0.197790 0.605362 -0.437344 -0.573307 0.107948 -1.436057 0.995390 \n",
"762 0.100350 0.689221 -0.950142 0.477153 -0.908423 0.109756 2.284619 \n",
".. ... ... ... ... ... ... ... \n",
"252 -0.407608 -0.441935 -1.985984 1.444090 0.083796 -0.670731 0.259822 \n",
"844 -0.961302 1.353622 0.111644 1.015867 -2.161583 0.255054 0.858101 \n",
"935 -1.612218 0.425603 0.450798 1.795192 -1.030650 0.071948 0.533019 \n",
"884 -0.166472 2.470375 -0.515409 0.360396 -1.543268 -1.070048 0.645882 \n",
"596 -1.135855 0.142313 0.627392 0.540314 0.120423 -1.359658 2.273243 \n",
"\n",
" X0 X1 \n",
"463 0.823336 1.016157 \n",
"425 1.623652 -1.751637 \n",
"615 2.090585 -1.071918 \n",
"842 0.995390 -1.436057 \n",
"762 2.284619 0.109756 \n",
".. ... ... \n",
"252 0.259822 -0.670731 \n",
"844 0.858101 0.255054 \n",
"935 0.533019 0.071948 \n",
"884 0.645882 -1.070048 \n",
"596 2.273243 -1.359658 \n",
"\n",
"[800 rows x 9 columns], 'y': 463 13.719677\n",
"425 13.323890\n",
"615 -8.516564\n",
"842 12.471850\n",
"762 17.175897\n",
" ... \n",
"252 -0.786820\n",
"844 -5.710027\n",
"935 7.518694\n",
"884 9.392570\n",
"596 14.773593\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 463 True\n",
"425 True\n",
"615 False\n",
"842 True\n",
"762 True\n",
" ... \n",
"252 False\n",
"844 False\n",
"935 True\n",
"884 True\n",
"596 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Control): 0.7588\n",
"INFO:causalml: Gini (Treatment): 0.9957\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1346\n",
"INFO:causalml: RMSE (Treatment): 0.7431\n",
"INFO:causalml: sMAPE (Control): 0.5441\n",
"INFO:causalml: sMAPE (Treatment): 0.1588\n",
"INFO:causalml: Gini (Control): 0.7470\n",
"INFO:causalml: Gini (Treatment): 0.9950\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"575 -1.620041 1.491617 -0.062211 2.124632 -1.193065 -0.186873 -1.131729 \n",
"619 -0.665633 0.888952 -1.242832 -1.692269 0.683929 -0.545882 -0.557084 \n",
"323 -1.225390 1.130518 -1.096882 -0.350487 -0.604044 0.001088 0.390490 \n",
"616 -1.830382 0.390144 1.344299 0.522921 -0.493492 -0.066279 -0.126978 \n",
"730 -0.464492 1.914988 0.441570 2.087402 0.778967 -0.037934 1.464653 \n",
".. ... ... ... ... ... ... ... \n",
"538 -1.000985 2.417859 0.027563 1.436945 1.891137 -2.054885 0.763508 \n",
"56 -0.181805 0.846647 -1.250481 -0.053526 -0.601944 -0.032988 0.712948 \n",
"135 -1.650996 2.260611 -1.131024 1.404627 -3.453988 0.098573 0.729032 \n",
"272 -3.198219 0.554460 0.179309 2.032221 -0.401181 -1.528464 1.415503 \n",
"372 -0.187672 1.029903 -0.511397 0.030915 0.302991 0.331046 2.442863 \n",
"\n",
" X0 X1 \n",
"575 -1.131729 -0.186873 \n",
"619 -0.557084 -0.545882 \n",
"323 0.390490 0.001088 \n",
"616 -0.126978 -0.066279 \n",
"730 1.464653 -0.037934 \n",
".. ... ... \n",
"538 0.763508 -2.054885 \n",
"56 0.712948 -0.032988 \n",
"135 0.729032 0.098573 \n",
"272 1.415503 -1.528464 \n",
"372 2.442863 0.331046 \n",
"\n",
"[800 rows x 9 columns], 'y': 575 1.988673\n",
"619 5.375176\n",
"323 6.435152\n",
"616 4.604313\n",
"730 20.892188\n",
" ... \n",
"538 16.761939\n",
"56 -1.924324\n",
"135 2.418843\n",
"272 -7.247960\n",
"372 20.420704\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 575 True\n",
"619 True\n",
"323 True\n",
"616 True\n",
"730 True\n",
" ... \n",
"538 True\n",
"56 False\n",
"135 True\n",
"272 False\n",
"372 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0101\n",
"INFO:causalml: RMSE (Treatment): 0.7229\n",
"INFO:causalml: sMAPE (Control): 0.5357\n",
"INFO:causalml: sMAPE (Treatment): 0.1439\n",
"INFO:causalml: Gini (Control): 0.7439\n",
"INFO:causalml: Gini (Treatment): 0.9947\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0507\n",
"INFO:causalml: RMSE (Treatment): 0.7382\n",
"INFO:causalml: sMAPE (Control): 0.5534\n",
"INFO:causalml: sMAPE (Treatment): 0.1506\n",
"INFO:causalml: Gini (Control): 0.7552\n",
"INFO:causalml: Gini (Treatment): 0.9949\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"198 0.659512 0.897440 0.294459 0.913949 0.778009 0.324835 -0.038025 \n",
"134 -2.667587 1.546152 0.275451 1.924880 -1.241239 -1.069396 2.735936 \n",
"705 0.261741 3.188563 -2.010226 -0.517940 1.029141 0.040541 0.750995 \n",
"787 0.106762 2.948496 -1.895033 -1.510659 -0.813685 -0.585402 -0.112157 \n",
"582 -1.856578 1.519025 -0.354817 1.777607 -0.103456 -0.540906 0.894554 \n",
".. ... ... ... ... ... ... ... \n",
"540 -1.436398 1.926302 -0.447886 -0.040584 2.181020 0.720121 1.462581 \n",
"720 -0.178107 0.545358 -1.188979 -2.009287 -1.712708 0.183073 2.293672 \n",
"729 -0.356672 0.996783 0.112431 0.185130 -0.025222 -0.843250 -0.270536 \n",
"623 -0.801864 -1.215830 -2.351636 1.814226 0.088183 -0.416848 1.256143 \n",
"500 -0.721612 -0.768412 0.628073 1.455824 1.152325 -0.160033 1.609167 \n",
"\n",
" X0 X1 \n",
"198 -0.038025 0.324835 \n",
"134 2.735936 -1.069396 \n",
"705 0.750995 0.040541 \n",
"787 -0.112157 -0.585402 \n",
"582 0.894554 -0.540906 \n",
".. ... ... \n",
"540 1.462581 0.720121 \n",
"720 2.293672 0.183073 \n",
"729 -0.270536 -0.843250 \n",
"623 1.256143 -0.416848 \n",
"500 1.609167 -0.160033 \n",
"\n",
"[800 rows x 9 columns], 'y': 198 16.522842\n",
"134 11.781552\n",
"705 18.039614\n",
"787 7.397373\n",
"582 10.549091\n",
" ... \n",
"540 19.517995\n",
"720 11.018665\n",
"729 8.272864\n",
"623 11.515329\n",
"500 17.859054\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 198 True\n",
"134 True\n",
"705 True\n",
"787 True\n",
"582 True\n",
" ... \n",
"540 True\n",
"720 True\n",
"729 True\n",
"623 True\n",
"500 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"359 -1.250892 0.608337 -1.159236 1.196291 -2.448155 -1.418204 2.171817 \n",
"900 -1.528877 0.854363 -0.020892 -0.125090 1.283825 0.408115 -0.168281 \n",
"710 -0.753746 2.934628 -1.147550 -0.196935 -0.921997 -0.625230 -0.292340 \n",
"551 -0.703300 0.665407 1.347444 -0.209059 -0.416380 -0.482778 -0.699907 \n",
"877 -0.359565 0.826991 -0.179359 -1.811258 1.418593 1.405163 -0.075609 \n",
".. ... ... ... ... ... ... ... \n",
"330 -1.641813 -0.968627 -0.550355 0.137721 0.993861 -3.243495 -0.204848 \n",
"41 0.075080 -0.116355 -0.810933 2.222729 -0.150383 0.082462 -0.018712 \n",
"772 -1.631390 0.465915 -1.149010 1.590925 -0.376748 -1.413846 -0.295266 \n",
"29 -1.026446 0.591365 0.862837 0.817784 -0.689067 -0.759426 -0.807769 \n",
"967 -1.386450 1.648145 -0.953457 1.161783 -0.494481 -3.161705 1.259562 \n",
"\n",
" X0 X1 \n",
"359 2.171817 -1.418204 \n",
"900 -0.168281 0.408115 \n",
"710 -0.292340 -0.625230 \n",
"551 -0.699907 -0.482778 \n",
"877 -0.075609 1.405163 \n",
".. ... ... \n",
"330 -0.204848 -3.243495 \n",
"41 -0.018712 0.082462 \n",
"772 -0.295266 -1.413846 \n",
"29 -0.807769 -0.759426 \n",
"967 1.259562 -3.161705 \n",
"\n",
"[800 rows x 9 columns], 'y': 359 7.556557\n",
"900 9.560536\n",
"710 5.904376\n",
"551 4.944574\n",
"877 12.698322\n",
" ... \n",
"330 -3.426282\n",
"41 12.011428\n",
"772 3.250030\n",
"29 3.429318\n",
"967 -2.657495\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 359 True\n",
"900 True\n",
"710 True\n",
"551 True\n",
"877 True\n",
" ... \n",
"330 False\n",
"41 True\n",
"772 True\n",
"29 True\n",
"967 False\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"819 -0.213838 0.707596 -1.514565 -0.247908 -0.337350 0.286133 2.415622 \n",
"951 -3.474533 0.798959 -1.342694 -0.344446 0.399229 -0.460764 1.828910 \n",
"138 0.549141 0.618552 -0.411559 1.549844 -1.097545 0.417703 -0.088796 \n",
"815 -1.745923 1.862168 -1.038948 0.258170 -1.036942 -0.586505 -0.189691 \n",
"689 -2.243570 1.275778 -1.424485 -2.440678 -1.906424 -2.373430 -0.198568 \n",
".. ... ... ... ... ... ... ... \n",
"404 0.269959 -1.426995 -0.624877 1.207414 -0.632554 0.826927 -0.342909 \n",
"28 0.355363 -1.712344 -0.541527 -1.500008 -0.178104 -0.523930 -0.313656 \n",
"54 1.459268 1.943608 1.129690 0.548664 -1.340973 -0.812905 -0.963531 \n",
"392 0.391364 -0.989100 -0.665144 -0.463719 1.025926 0.692766 -0.147856 \n",
"830 -0.930911 -0.544095 0.223413 2.002798 -0.116908 -1.217341 0.878953 \n",
"\n",
" X0 X1 \n",
"819 2.415622 0.286133 \n",
"951 1.828910 -0.460764 \n",
"138 -0.088796 0.417703 \n",
"815 -0.189691 -0.586505 \n",
"689 -0.198568 -2.373430 \n",
".. ... ... \n",
"404 -0.342909 0.826927 \n",
"28 -0.313656 -0.523930 \n",
"54 -0.963531 -0.812905 \n",
"392 -0.147856 0.692766 \n",
"830 0.878953 -1.217341 \n",
"\n",
"[800 rows x 9 columns], 'y': 819 17.249427\n",
"951 -9.429193\n",
"138 11.274922\n",
"815 2.533898\n",
"689 -13.994008\n",
" ... \n",
"404 -1.323791\n",
"28 4.753158\n",
"54 9.737109\n",
"392 12.111471\n",
"830 -1.062782\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 819 True\n",
"951 False\n",
"138 True\n",
"815 True\n",
"689 False\n",
" ... \n",
"404 False\n",
"28 True\n",
"54 True\n",
"392 True\n",
"830 False\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Control): 3.0887\n",
"INFO:causalml: RMSE (Treatment): 0.7302\n",
"INFO:causalml: sMAPE (Control): 0.5598\n",
"INFO:causalml: sMAPE (Treatment): 0.1484\n",
"INFO:causalml: Gini (Control): 0.7513\n",
"INFO:causalml: Gini (Treatment): 0.9947\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0181\n",
"INFO:causalml: RMSE (Treatment): 0.7458\n",
"INFO:causalml: sMAPE (Control): 0.5518\n",
"INFO:causalml: sMAPE (Treatment): 0.1555\n",
"INFO:causalml: Gini (Control): 0.7430\n",
"INFO:causalml: Gini (Treatment): 0.9943\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0144\n",
"INFO:causalml: RMSE (Treatment): 0.6481\n",
"INFO:causalml: sMAPE (Control): 0.5533\n",
"INFO:causalml: sMAPE (Treatment): 0.1329\n",
"INFO:causalml: Gini (Control): 0.7747\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"843 -1.819689 2.321605 1.205835 1.378205 -0.687589 -1.421073 -0.008063 \n",
"171 -1.142720 1.363980 -1.309646 -0.806699 -1.397706 0.591713 -0.245067 \n",
"554 -1.733641 -1.598375 1.607236 0.161280 -1.096390 -2.406156 -0.029761 \n",
"343 -1.245795 1.748387 0.828184 -1.775403 -0.972468 -0.866709 0.489782 \n",
"19 -0.472917 -0.200341 -0.794933 2.274586 -1.165631 -1.449257 0.217406 \n",
".. ... ... ... ... ... ... ... \n",
"823 0.865119 0.256833 -0.057368 0.192438 0.700995 -1.560342 1.492618 \n",
"456 -1.180741 1.678642 0.789419 1.087730 0.037308 0.563234 0.500563 \n",
"913 -1.875782 0.226743 -0.219598 2.054710 0.145892 -0.134735 0.897215 \n",
"74 -0.996694 0.160173 -2.637286 -2.023282 1.303478 -1.990375 1.207325 \n",
"424 -1.778586 -0.041397 0.547042 -0.146728 -0.207960 0.620439 1.840962 \n",
"\n",
" X0 X1 \n",
"843 -0.008063 -1.421073 \n",
"171 -0.245067 0.591713 \n",
"554 -0.029761 -2.406156 \n",
"343 0.489782 -0.866709 \n",
"19 0.217406 -1.449257 \n",
".. ... ... \n",
"823 1.492618 -1.560342 \n",
"456 0.500563 0.563234 \n",
"913 0.897215 -0.134735 \n",
"74 1.207325 -1.990375 \n",
"424 1.840962 0.620439 \n",
"\n",
"[800 rows x 9 columns], 'y': 843 6.032900\n",
"171 2.573635\n",
"554 -8.661899\n",
"343 4.731928\n",
"19 6.700542\n",
" ... \n",
"823 18.415087\n",
"456 12.752919\n",
"913 10.638397\n",
"74 8.688916\n",
"424 -5.690040\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 843 True\n",
"171 True\n",
"554 False\n",
"343 True\n",
"19 True\n",
" ... \n",
"823 True\n",
"456 True\n",
"913 True\n",
"74 True\n",
"424 False\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"943 -2.252890 0.804815 -1.189588 0.399429 -1.924218 -1.450234 0.482038 \n",
"348 -0.822689 1.950660 -1.773724 0.367062 -1.388543 -1.489743 1.572649 \n",
"940 1.618120 -0.305935 -1.002070 0.408436 -0.407028 -1.124055 0.103301 \n",
"406 -1.787177 0.601264 -0.241539 -0.288932 0.204519 1.070860 0.778961 \n",
"739 -1.946497 0.850165 -0.030473 0.710454 0.237677 -0.545032 -0.878717 \n",
".. ... ... ... ... ... ... ... \n",
"453 -1.249839 -0.644978 -0.359153 1.084902 -0.516596 -0.365041 -0.139304 \n",
"583 -0.820218 -0.945137 -1.568877 -0.082810 0.614237 -0.346996 0.919443 \n",
"963 -1.400546 1.656134 -0.736065 1.479252 -1.067357 -1.225327 0.845018 \n",
"625 -1.336550 0.261883 0.557248 -0.069210 1.161863 -0.761143 1.075224 \n",
"375 -1.851590 -0.485257 -0.505663 -0.368564 -1.023961 -1.380998 1.320727 \n",
"\n",
" X0 X1 \n",
"943 0.482038 -1.450234 \n",
"348 1.572649 -1.489743 \n",
"940 0.103301 -1.124055 \n",
"406 0.778961 1.070860 \n",
"739 -0.878717 -0.545032 \n",
".. ... ... \n",
"453 -0.139304 -0.365041 \n",
"583 0.919443 -0.346996 \n",
"963 0.845018 -1.225327 \n",
"625 1.075224 -0.761143 \n",
"375 1.320727 -1.380998 \n",
"\n",
"[800 rows x 9 columns], 'y': 943 -10.951371\n",
"348 9.469476\n",
"940 12.297833\n",
"406 9.585905\n",
"739 -3.295801\n",
" ... \n",
"453 4.442757\n",
"583 -2.819370\n",
"963 7.905661\n",
"625 12.619755\n",
"375 3.740574\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 943 False\n",
"348 True\n",
"940 True\n",
"406 True\n",
"739 False\n",
" ... \n",
"453 True\n",
"583 False\n",
"963 True\n",
"625 True\n",
"375 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Treatment): 0.9962\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0228\n",
"INFO:causalml: RMSE (Treatment): 0.6860\n",
"INFO:causalml: sMAPE (Control): 0.5510\n",
"INFO:causalml: sMAPE (Treatment): 0.1466\n",
"INFO:causalml: Gini (Control): 0.7470\n",
"INFO:causalml: Gini (Treatment): 0.9953\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9469\n",
"INFO:causalml: RMSE (Treatment): 0.7414\n",
"INFO:causalml: sMAPE (Control): 0.5231\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"364 -1.078590 1.563574 1.504080 -0.486241 -1.540033 -0.435152 0.956055 \n",
"932 -1.213129 0.643221 -0.007752 0.320722 -1.204954 0.065085 -0.764774 \n",
"634 -0.085908 0.582399 -1.871995 -0.128561 1.399274 -2.013647 -0.399001 \n",
"840 0.863115 0.884267 0.385888 1.081802 -0.236509 -0.114485 0.397843 \n",
"671 -1.805567 1.966280 0.355705 0.675031 -1.531579 -0.217720 -0.701737 \n",
".. ... ... ... ... ... ... ... \n",
"616 -1.830382 0.390144 1.344299 0.522921 -0.493492 -0.066279 -0.126978 \n",
"850 -1.559250 1.506724 0.449602 0.956745 -1.983230 -0.410145 2.749812 \n",
"144 -0.199659 1.050489 1.034230 1.426123 0.412507 0.336879 2.230533 \n",
"558 -0.550544 0.440434 -0.750181 1.612420 0.551244 0.168593 1.698572 \n",
"843 -1.819689 2.321605 1.205835 1.378205 -0.687589 -1.421073 -0.008063 \n",
"\n",
" X0 X1 \n",
"364 0.956055 -0.435152 \n",
"932 -0.764774 0.065085 \n",
"634 -0.399001 -2.013647 \n",
"840 0.397843 -0.114485 \n",
"671 -0.701737 -0.217720 \n",
".. ... ... \n",
"616 -0.126978 -0.066279 \n",
"850 2.749812 -0.410145 \n",
"144 2.230533 0.336879 \n",
"558 1.698572 0.168593 \n",
"843 -0.008063 -1.421073 \n",
"\n",
"[800 rows x 9 columns], 'y': 364 7.693260\n",
"932 1.654133\n",
"634 8.990907\n",
"840 15.748095\n",
"671 -6.150284\n",
" ... \n",
"616 4.604313\n",
"850 12.795708\n",
"144 22.522110\n",
"558 18.249687\n",
"843 6.032900\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 364 True\n",
"932 True\n",
"634 True\n",
"840 True\n",
"671 False\n",
" ... \n",
"616 True\n",
"850 True\n",
"144 True\n",
"558 True\n",
"843 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"368 -2.298170 1.146523 -1.218792 -0.454870 -0.475421 -1.530454 0.327366 \n",
"156 -1.847964 1.488048 -0.079643 0.746560 -1.203496 -0.411978 0.865581 \n",
"728 0.021921 2.347870 1.993858 -2.023822 -0.508237 -0.879659 0.131258 \n",
"822 -0.308029 1.239229 -1.712106 2.618474 -0.731033 -0.360836 1.878272 \n",
"503 -2.066140 2.033010 -1.559405 0.352297 0.418520 -0.333911 1.084480 \n",
".. ... ... ... ... ... ... ... \n",
"805 -2.622025 -0.308385 -0.881486 -0.183280 0.289018 -2.986494 0.245462 \n",
"25 -0.690786 1.699003 -0.408257 0.040733 -2.094357 -0.544787 1.407147 \n",
"402 -2.267259 2.356874 -0.312747 -0.267666 -0.938556 0.873383 1.145311 \n",
"643 -0.837543 2.690282 -0.956587 1.519004 -3.148478 1.188059 1.128666 \n",
"962 -0.058529 0.632542 -0.738886 -0.663994 -0.868324 -2.555294 -0.708468 \n",
"\n",
" X0 X1 \n",
"368 0.327366 -1.530454 \n",
"156 0.865581 -0.411978 \n",
"728 0.131258 -0.879659 \n",
"822 1.878272 -0.360836 \n",
"503 1.084480 -0.333911 \n",
".. ... ... \n",
"805 0.245462 -2.986494 \n",
"25 1.407147 -0.544787 \n",
"402 1.145311 0.873383 \n",
"643 1.128666 1.188059 \n",
"962 -0.708468 -2.555294 \n",
"\n",
"[800 rows x 9 columns], 'y': 368 1.367327\n",
"156 6.574194\n",
"728 9.374205\n",
"822 17.217532\n",
"503 -3.163402\n",
" ... \n",
"805 -0.721618\n",
"25 8.623876\n",
"402 8.213659\n",
"643 9.118838\n",
"962 1.353868\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 368 True\n",
"156 True\n",
"728 True\n",
"822 True\n",
"503 False\n",
" ... \n",
"805 True\n",
"25 True\n",
"402 True\n",
"643 True\n",
"962 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Treatment): 0.1503\n",
"INFO:causalml: Gini (Control): 0.7236\n",
"INFO:causalml: Gini (Treatment): 0.9936\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0841\n",
"INFO:causalml: RMSE (Treatment): 0.7612\n",
"INFO:causalml: sMAPE (Control): 0.5448\n",
"INFO:causalml: sMAPE (Treatment): 0.1577\n",
"INFO:causalml: Gini (Control): 0.7382\n",
"INFO:causalml: Gini (Treatment): 0.9938\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1236\n",
"INFO:causalml: RMSE (Treatment): 0.7095\n",
"INFO:causalml: sMAPE (Control): 0.5382\n",
"INFO:causalml: sMAPE (Treatment): 0.1403\n",
"INFO:causalml: Gini (Control): 0.7091\n",
"INFO:causalml: Gini (Treatment): 0.9948\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"647 0.107638 1.405276 -0.825606 1.040288 -0.390118 1.171509 -0.898491 \n",
"656 -1.273620 1.797036 2.092367 0.953316 1.197489 -0.625639 0.731893 \n",
"704 -2.163862 1.694504 -1.310167 0.084654 -0.510292 -1.743878 -0.389615 \n",
"691 -0.985721 1.895540 0.176961 1.219720 -1.233766 -2.238988 1.283650 \n",
"665 -2.174730 0.248624 -0.092817 -0.715735 0.210519 -0.742166 -0.077510 \n",
".. ... ... ... ... ... ... ... \n",
"187 0.166212 1.756207 0.174379 0.739862 -0.309621 0.009743 -0.960418 \n",
"139 -1.494898 1.076908 0.509008 -0.008836 0.810677 -0.868950 0.996169 \n",
"86 -1.171030 0.999708 -0.474575 -0.002353 1.214732 -0.432952 -1.419492 \n",
"981 -1.677948 -1.622248 -1.171652 0.349836 -2.213015 1.101661 0.722546 \n",
"435 -1.214834 1.792454 -0.114462 0.368086 -0.765766 0.747054 1.689047 \n",
"\n",
" X0 X1 \n",
"647 -0.898491 1.171509 \n",
"656 0.731893 -0.625639 \n",
"704 -0.389615 -1.743878 \n",
"691 1.283650 -2.238988 \n",
"665 -0.077510 -0.742166 \n",
".. ... ... \n",
"187 -0.960418 0.009743 \n",
"139 0.996169 -0.868950 \n",
"86 -1.419492 -0.432952 \n",
"981 0.722546 1.101661 \n",
"435 1.689047 0.747054 \n",
"\n",
"[800 rows x 9 columns], 'y': 647 9.825764\n",
"656 15.632059\n",
"704 0.037896\n",
"691 9.514980\n",
"665 2.331887\n",
" ... \n",
"187 9.138120\n",
"139 11.778033\n",
"86 4.893758\n",
"981 -12.765797\n",
"435 13.857254\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 647 True\n",
"656 True\n",
"704 True\n",
"691 True\n",
"665 True\n",
" ... \n",
"187 True\n",
"139 True\n",
"86 True\n",
"981 False\n",
"435 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"914 -0.033438 1.223195 0.288715 -0.542490 -0.948134 0.723902 1.010993 \n",
"658 -0.901419 0.202237 -2.133776 0.712530 -0.236428 -0.743572 0.753088 \n",
"366 -1.403735 1.313386 1.988391 -0.578266 1.442075 1.501325 0.150474 \n",
"775 -2.360643 1.573135 -1.344099 0.823569 -1.137799 -0.227823 0.357754 \n",
"519 -0.928551 1.643629 -0.012840 -0.089474 -1.289071 -0.902553 0.551816 \n",
".. ... ... ... ... ... ... ... \n",
"216 -1.185130 1.723057 -2.436984 -0.947938 -0.797647 -1.237892 0.020300 \n",
"121 1.534365 1.840154 -0.218035 -0.257249 -0.734629 -0.767329 -1.295119 \n",
"495 -0.937219 0.875867 0.303746 0.659802 -0.598222 1.107266 -0.420269 \n",
"619 -0.665633 0.888952 -1.242832 -1.692269 0.683929 -0.545882 -0.557084 \n",
"942 -1.242170 1.302580 -0.669569 -0.436052 -0.873213 -1.051785 0.880964 \n",
"\n",
" X0 X1 \n",
"914 1.010993 0.723902 \n",
"658 0.753088 -0.743572 \n",
"366 0.150474 1.501325 \n",
"775 0.357754 -0.227823 \n",
"519 0.551816 -0.902553 \n",
".. ... ... \n",
"216 0.020300 -1.237892 \n",
"121 -1.295119 -0.767329 \n",
"495 -0.420269 1.107266 \n",
"619 -0.557084 -0.545882 \n",
"942 0.880964 -1.051785 \n",
"\n",
"[800 rows x 9 columns], 'y': 914 12.801840\n",
"658 8.483868\n",
"366 13.879918\n",
"775 -7.934090\n",
"519 6.513308\n",
" ... \n",
"216 2.376140\n",
"121 8.541463\n",
"495 7.428448\n",
"619 5.375176\n",
"942 6.530430\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 914 True\n",
"658 True\n",
"366 True\n",
"775 False\n",
"519 True\n",
" ... \n",
"216 True\n",
"121 True\n",
"495 True\n",
"619 True\n",
"942 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"641 -2.239311 2.194117 -0.353902 0.360330 0.632691 -1.221853 -0.388297 \n",
"470 -0.119189 2.038863 -0.185731 -0.858254 -2.226518 -0.985301 -1.027339 \n",
"507 -1.568032 -0.180577 -1.663979 0.966254 -1.411487 -2.027152 0.819706 \n",
"826 -1.770524 1.148010 -0.657719 0.002402 -0.619052 -2.314279 0.680449 \n",
"464 -1.458938 1.066085 -0.038658 -0.458790 0.563506 0.649619 0.007999 \n",
".. ... ... ... ... ... ... ... \n",
"754 -1.008916 0.089333 -0.381037 1.182553 0.653293 -0.475920 0.036262 \n",
"305 -2.363560 1.924215 1.711203 -1.273183 -0.441145 -0.403834 1.202634 \n",
"855 -1.525195 2.679573 0.256933 -0.844142 -0.237770 -0.840204 0.824960 \n",
"248 -0.322096 0.956739 -1.722687 0.445099 1.192956 -0.951015 -0.322611 \n",
"649 -2.154564 1.280369 -0.834181 1.662134 -0.490512 0.051132 1.668837 \n",
"\n",
" X0 X1 \n",
"641 -0.388297 -1.221853 \n",
"470 -1.027339 -0.985301 \n",
"507 0.819706 -2.027152 \n",
"826 0.680449 -2.314279 \n",
"464 0.007999 0.649619 \n",
".. ... ... \n",
"754 0.036262 -0.475920 \n",
"305 1.202634 -0.403834 \n",
"855 0.824960 -0.840204 \n",
"248 -0.322611 -0.951015 \n",
"649 1.668837 0.051132 \n",
"\n",
"[800 rows x 9 columns], 'y': 641 4.862547\n",
"470 -0.021734\n",
"507 -8.258449\n",
"826 3.753175\n",
"464 8.601975\n",
" ... \n",
"754 9.673575\n",
"305 7.299002\n",
"855 8.939223\n",
"248 10.511960\n",
"649 11.515556\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 641 True\n",
"470 True\n",
"507 False\n",
"826 True\n",
"464 True\n",
" ... \n",
"754 True\n",
"305 True\n",
"855 True\n",
"248 True\n",
"649 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Control): 3.0128\n",
"INFO:causalml: RMSE (Treatment): 0.7183\n",
"INFO:causalml: sMAPE (Control): 0.5521\n",
"INFO:causalml: sMAPE (Treatment): 0.1389\n",
"INFO:causalml: Gini (Control): 0.7298\n",
"INFO:causalml: Gini (Treatment): 0.9944\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1668\n",
"INFO:causalml: RMSE (Treatment): 0.7702\n",
"INFO:causalml: sMAPE (Control): 0.5480\n",
"INFO:causalml: sMAPE (Treatment): 0.1526\n",
"INFO:causalml: Gini (Control): 0.7192\n",
"INFO:causalml: Gini (Treatment): 0.9944\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9965\n",
"INFO:causalml: RMSE (Treatment): 0.7273\n",
"INFO:causalml: sMAPE (Control): 0.5443\n",
"INFO:causalml: sMAPE (Treatment): 0.1442\n",
"INFO:causalml: Gini (Control): 0.7526\n",
"INFO:causalml: Gini (Treatment): 0.9950\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"897 -0.501089 -1.031997 -1.334448 -0.012652 -0.451207 -0.315850 -0.369898 \n",
"172 -1.078399 1.722034 -1.675423 -0.026819 -0.577141 -1.633102 1.619526 \n",
"20 -0.810576 1.360309 -0.656243 1.132048 -0.926051 0.651433 1.648609 \n",
"323 -1.225390 1.130518 -1.096882 -0.350487 -0.604044 0.001088 0.390490 \n",
"900 -1.528877 0.854363 -0.020892 -0.125090 1.283825 0.408115 -0.168281 \n",
".. ... ... ... ... ... ... ... \n",
"779 -1.245329 0.830018 0.144683 0.171689 0.322303 -0.800108 -0.118539 \n",
"556 -1.361029 0.354536 -0.575247 -1.264914 -0.170182 -2.098308 -0.219006 \n",
"461 -1.364660 -1.842826 0.241333 0.373454 -0.367738 0.860921 0.880397 \n",
"958 -0.495343 0.847053 -0.028871 -0.718100 -0.871586 -0.630299 -0.132512 \n",
"49 -0.982952 2.225363 -1.526931 -0.694241 0.029243 -0.455032 0.234971 \n",
"\n",
" X0 X1 \n",
"897 -0.369898 -0.315850 \n",
"172 1.619526 -1.633102 \n",
"20 1.648609 0.651433 \n",
"323 0.390490 0.001088 \n",
"900 -0.168281 0.408115 \n",
".. ... ... \n",
"779 -0.118539 -0.800108 \n",
"556 -0.219006 -2.098308 \n",
"461 0.880397 0.860921 \n",
"958 -0.132512 -0.630299 \n",
"49 0.234971 -0.455032 \n",
"\n",
"[800 rows x 9 columns], 'y': 897 -4.566144\n",
"172 10.160763\n",
"20 14.509975\n",
"323 6.435152\n",
"900 9.560536\n",
" ... \n",
"779 7.001850\n",
"556 0.746452\n",
"461 -6.434741\n",
"958 5.051010\n",
"49 8.274390\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 897 False\n",
"172 True\n",
"20 True\n",
"323 True\n",
"900 True\n",
" ... \n",
"779 True\n",
"556 True\n",
"461 False\n",
"958 True\n",
"49 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"979 -1.320009 -0.547319 0.058828 1.048133 1.078373 -0.857583 0.480911 \n",
"786 1.321150 2.395540 -1.313403 1.003881 -1.291792 -0.548760 -1.106383 \n",
"809 -1.846180 1.505397 0.539237 1.200542 -1.354442 -0.559294 0.909066 \n",
"941 -0.858300 2.689664 0.060630 -0.800292 -0.649474 -0.542851 0.680252 \n",
"342 -1.702353 -0.506216 -1.938204 -0.079481 -0.042667 -1.379694 0.242173 \n",
".. ... ... ... ... ... ... ... \n",
"371 -2.437457 1.813953 -0.635430 -0.294641 -0.408002 0.338137 0.274353 \n",
"299 -1.370319 0.104371 -0.773003 1.741095 0.119556 -1.227923 -0.272634 \n",
"234 0.299190 -0.331887 -1.279492 1.419962 -1.114102 -1.432604 0.304340 \n",
"953 -2.210788 0.834261 -1.790993 0.421272 -1.301499 -0.508087 -0.600657 \n",
"365 -0.079718 -0.263842 0.747338 0.929554 0.426811 -1.590757 1.366447 \n",
"\n",
" X0 X1 \n",
"979 0.480911 -0.857583 \n",
"786 -1.106383 -0.548760 \n",
"809 0.909066 -0.559294 \n",
"941 0.680252 -0.542851 \n",
"342 0.242173 -1.379694 \n",
".. ... ... \n",
"371 0.274353 0.338137 \n",
"299 -0.272634 -1.227923 \n",
"234 0.304340 -1.432604 \n",
"953 -0.600657 -0.508087 \n",
"365 1.366447 -1.590757 \n",
"\n",
"[800 rows x 9 columns], 'y': 979 10.393456\n",
"786 8.954078\n",
"809 7.084273\n",
"941 9.630585\n",
"342 -6.856146\n",
" ... \n",
"371 4.468847\n",
"299 5.614664\n",
"234 7.980017\n",
"953 -9.484729\n",
"365 15.195618\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 979 True\n",
"786 True\n",
"809 True\n",
"941 True\n",
"342 False\n",
" ... \n",
"371 True\n",
"299 True\n",
"234 True\n",
"953 False\n",
"365 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1210\n",
"INFO:causalml: RMSE (Treatment): 0.6654\n",
"INFO:causalml: sMAPE (Control): 0.5705\n",
"INFO:causalml: sMAPE (Treatment): 0.1382\n",
"INFO:causalml: Gini (Control): 0.7480\n",
"INFO:causalml: Gini (Treatment): 0.9959\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0215\n",
"INFO:causalml: RMSE (Treatment): 0.6435\n",
"INFO:causalml: sMAPE (Control): 0.5569\n",
"INFO:causalml: sMAPE (Treatment): 0.1369\n",
"INFO:causalml: Gini (Control): 0.7462\n",
"INFO:causalml: Gini (Treatment): 0.9960\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"158 -0.957173 1.371619 -0.480406 1.601165 -1.831360 -1.142136 2.213026 \n",
"970 -1.394364 1.786996 1.473136 -0.626023 -0.659760 -1.513833 -0.140064 \n",
"299 -1.370319 0.104371 -0.773003 1.741095 0.119556 -1.227923 -0.272634 \n",
"338 0.024125 2.398870 -0.344341 0.049499 -0.135206 -1.931463 1.292749 \n",
"490 -2.391954 2.550940 -0.776817 0.245312 0.292239 1.475404 1.305845 \n",
".. ... ... ... ... ... ... ... \n",
"875 -2.411078 0.159398 0.119580 0.748787 -0.457547 0.313736 -0.560625 \n",
"940 1.618120 -0.305935 -1.002070 0.408436 -0.407028 -1.124055 0.103301 \n",
"38 -0.603358 0.428043 -1.045056 -0.064250 -1.028287 -0.614795 2.905953 \n",
"528 -1.220354 1.951022 -0.143826 0.399718 -1.725250 0.579914 0.826622 \n",
"126 -0.113592 2.137890 0.587189 -0.608982 -0.434552 -1.535190 0.152988 \n",
"\n",
" X0 X1 \n",
"158 2.213026 -1.142136 \n",
"970 -0.140064 -1.513833 \n",
"299 -0.272634 -1.227923 \n",
"338 1.292749 -1.931463 \n",
"490 1.305845 1.475404 \n",
".. ... ... \n",
"875 -0.560625 0.313736 \n",
"940 0.103301 -1.124055 \n",
"38 2.905953 -0.614795 \n",
"528 0.826622 0.579914 \n",
"126 0.152988 -1.535190 \n",
"\n",
"[800 rows x 9 columns], 'y': 158 12.243766\n",
"970 3.837965\n",
"299 5.614664\n",
"338 14.613827\n",
"490 -3.562790\n",
" ... \n",
"875 1.205333\n",
"940 12.297833\n",
"38 15.173396\n",
"528 8.156158\n",
"126 9.175909\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 158 True\n",
"970 True\n",
"299 True\n",
"338 True\n",
"490 False\n",
" ... \n",
"875 True\n",
"940 True\n",
"38 True\n",
"528 True\n",
"126 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"981 -1.677948 -1.622248 -1.171652 0.349836 -2.213015 1.101661 0.722546 \n",
"610 -0.711808 1.595194 0.094204 0.005941 -1.302585 -0.669101 1.030998 \n",
"356 -1.087973 1.063452 -0.582690 -0.391019 -0.717769 0.567377 0.968256 \n",
"829 -1.396963 -0.080609 -2.047189 -0.910429 -1.164537 -1.251936 -0.347075 \n",
"756 0.646687 0.512998 -0.425760 0.363156 -1.685429 -1.135889 1.931878 \n",
".. ... ... ... ... ... ... ... \n",
"454 -0.487418 0.412049 -0.859189 0.620612 -1.830073 -0.234510 2.177056 \n",
"489 -0.365082 -1.451807 -0.737839 0.617674 0.183613 -0.899562 -1.117287 \n",
"917 -2.304123 1.615978 1.217869 0.195346 -0.128304 -2.287419 1.444336 \n",
"371 -2.437457 1.813953 -0.635430 -0.294641 -0.408002 0.338137 0.274353 \n",
"936 -0.482737 -0.826928 -0.542605 -0.831002 -1.069918 -2.562988 1.258835 \n",
"\n",
" X0 X1 \n",
"981 0.722546 1.101661 \n",
"610 1.030998 -0.669101 \n",
"356 0.968256 0.567377 \n",
"829 -0.347075 -1.251936 \n",
"756 1.931878 -1.135889 \n",
".. ... ... \n",
"454 2.177056 -0.234510 \n",
"489 -1.117287 -0.899562 \n",
"917 1.444336 -2.287419 \n",
"371 0.274353 0.338137 \n",
"936 1.258835 -2.562988 \n",
"\n",
"[800 rows x 9 columns], 'y': 981 -12.765797\n",
"610 9.248446\n",
"356 -4.713297\n",
"829 -9.531238\n",
"756 13.898148\n",
" ... \n",
"454 12.121477\n",
"489 2.992182\n",
"917 -4.386132\n",
"371 4.468847\n",
"936 4.998172\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 981 False\n",
"610 True\n",
"356 False\n",
"829 False\n",
"756 True\n",
" ... \n",
"454 True\n",
"489 True\n",
"917 False\n",
"371 True\n",
"936 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0191\n",
"INFO:causalml: RMSE (Treatment): 0.7000\n",
"INFO:causalml: sMAPE (Control): 0.5560\n",
"INFO:causalml: sMAPE (Treatment): 0.1486\n",
"INFO:causalml: Gini (Control): 0.7625\n",
"INFO:causalml: Gini (Treatment): 0.9959\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0636\n",
"INFO:causalml: RMSE (Treatment): 0.7485\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"860 -0.100403 1.765904 -1.580557 -0.337570 0.115587 -0.811085 -0.628878 \n",
"897 -0.501089 -1.031997 -1.334448 -0.012652 -0.451207 -0.315850 -0.369898 \n",
"758 -0.238995 0.258464 -0.815419 0.855917 -0.016059 -1.265210 1.154044 \n",
"0 -1.124599 0.284335 -0.838580 1.223258 -0.089772 -0.633368 -1.372310 \n",
"521 -1.235550 2.140683 -2.152255 0.310299 -1.837627 -0.705293 -0.465131 \n",
".. ... ... ... ... ... ... ... \n",
"818 -2.294772 1.081263 -0.110948 0.240523 -2.452061 -0.676310 1.024324 \n",
"822 -0.308029 1.239229 -1.712106 2.618474 -0.731033 -0.360836 1.878272 \n",
"540 -1.436398 1.926302 -0.447886 -0.040584 2.181020 0.720121 1.462581 \n",
"383 -1.820459 0.011031 -1.003686 -0.301110 0.254573 -1.724935 0.863482 \n",
"708 -0.500479 1.096561 0.068238 -0.285794 -0.225941 -1.796636 1.564528 \n",
"\n",
" X0 X1 \n",
"860 -0.628878 -0.811085 \n",
"897 -0.369898 -0.315850 \n",
"758 1.154044 -1.265210 \n",
"0 -1.372310 -0.633368 \n",
"521 -0.465131 -0.705293 \n",
".. ... ... \n",
"818 1.024324 -0.676310 \n",
"822 1.878272 -0.360836 \n",
"540 1.462581 0.720121 \n",
"383 0.863482 -1.724935 \n",
"708 1.564528 -1.796636 \n",
"\n",
"[800 rows x 9 columns], 'y': 860 7.407446\n",
"897 -4.566144\n",
"758 12.802985\n",
"0 2.034000\n",
"521 0.541061\n",
" ... \n",
"818 -11.770683\n",
"822 17.217532\n",
"540 19.517995\n",
"383 5.450868\n",
"708 12.369009\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 860 True\n",
"897 False\n",
"758 True\n",
"0 True\n",
"521 True\n",
" ... \n",
"818 False\n",
"822 True\n",
"540 True\n",
"383 True\n",
"708 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"504 -1.822921 2.092858 0.863527 -0.530852 -1.148241 -0.820791 0.423434 \n",
"634 -0.085908 0.582399 -1.871995 -0.128561 1.399274 -2.013647 -0.399001 \n",
"127 -1.184442 1.633850 -0.489806 0.806896 -0.654125 -0.324203 -1.297638 \n",
"542 0.627854 2.166200 -1.384347 1.110795 -0.604388 0.513683 -0.468804 \n",
"370 -1.330248 0.617371 -1.086532 0.939956 -1.523047 -0.029272 0.894390 \n",
".. ... ... ... ... ... ... ... \n",
"793 -0.358699 1.859517 -0.250568 -1.297332 -1.747403 -0.402763 1.328310 \n",
"365 -0.079718 -0.263842 0.747338 0.929554 0.426811 -1.590757 1.366447 \n",
"608 0.655518 2.888275 -1.833586 -1.459779 0.528316 1.313325 -0.562842 \n",
"781 -1.133150 1.232298 -0.515253 0.174776 -0.131612 -1.374035 0.690224 \n",
"649 -2.154564 1.280369 -0.834181 1.662134 -0.490512 0.051132 1.668837 \n",
"\n",
" X0 X1 \n",
"504 0.423434 -0.820791 \n",
"634 -0.399001 -2.013647 \n",
"127 -1.297638 -0.324203 \n",
"542 -0.468804 0.513683 \n",
"370 0.894390 -0.029272 \n",
".. ... ... \n",
"793 1.328310 -0.402763 \n",
"365 1.366447 -1.590757 \n",
"608 -0.562842 1.313325 \n",
"781 0.690224 -1.374035 \n",
"649 1.668837 0.051132 \n",
"\n",
"[800 rows x 9 columns], 'y': 504 4.307409\n",
"634 8.990907\n",
"127 2.218793\n",
"542 12.169721\n",
"370 6.533270\n",
" ... \n",
"793 9.029191\n",
"365 15.195618\n",
"608 13.248344\n",
"781 8.454186\n",
"649 11.515556\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 504 True\n",
"634 True\n",
"127 True\n",
"542 True\n",
"370 True\n",
" ... \n",
"793 True\n",
"365 True\n",
"608 True\n",
"781 True\n",
"649 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Control): 0.5449\n",
"INFO:causalml: sMAPE (Treatment): 0.1569\n",
"INFO:causalml: Gini (Control): 0.7369\n",
"INFO:causalml: Gini (Treatment): 0.9940\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0360\n",
"INFO:causalml: RMSE (Treatment): 0.6878\n",
"INFO:causalml: sMAPE (Control): 0.5334\n",
"INFO:causalml: sMAPE (Treatment): 0.1338\n",
"INFO:causalml: Gini (Control): 0.7495\n",
"INFO:causalml: Gini (Treatment): 0.9950\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"175 -1.154340 0.806588 -0.662689 0.887440 0.111731 -1.009443 2.862545 \n",
"357 -0.393043 -0.896794 -0.679593 -0.767907 -0.184818 0.503640 -0.556493 \n",
"833 0.105901 0.316176 0.723477 1.307736 -0.792919 -1.127981 1.166249 \n",
"189 0.449357 0.147778 1.294543 -0.691706 -3.244261 -0.423599 0.935502 \n",
"544 -1.005914 0.255475 -1.627301 1.838873 0.013614 -1.474838 1.050279 \n",
".. ... ... ... ... ... ... ... \n",
"289 0.772794 0.012983 -0.390292 0.616515 1.060846 -1.481334 1.630755 \n",
"695 -0.197821 -0.512162 0.067361 0.935517 -0.381064 -1.154606 0.317349 \n",
"677 -0.926078 0.166307 -2.183101 1.988610 -0.286812 -1.952046 0.131363 \n",
"607 -0.713550 -0.806284 -0.339457 -0.279553 -0.477920 -0.690686 -0.221325 \n",
"334 -0.156694 0.911286 0.765684 0.401905 -1.416657 -2.667282 -1.314160 \n",
"\n",
" X0 X1 \n",
"175 2.862545 -1.009443 \n",
"357 -0.556493 0.503640 \n",
"833 1.166249 -1.127981 \n",
"189 0.935502 -0.423599 \n",
"544 1.050279 -1.474838 \n",
".. ... ... \n",
"289 1.630755 -1.481334 \n",
"695 0.317349 -1.154606 \n",
"677 0.131363 -1.952046 \n",
"607 -0.221325 -0.690686 \n",
"334 -1.314160 -2.667282 \n",
"\n",
"[800 rows x 9 columns], 'y': 175 17.721185\n",
"357 4.629609\n",
"833 13.445887\n",
"189 5.702358\n",
"544 10.741956\n",
" ... \n",
"289 19.767563\n",
"695 8.766754\n",
"677 6.044637\n",
"607 3.571733\n",
"334 -0.271330\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 175 True\n",
"357 True\n",
"833 True\n",
"189 True\n",
"544 True\n",
" ... \n",
"289 True\n",
"695 True\n",
"677 True\n",
"607 True\n",
"334 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0888\n",
"INFO:causalml: RMSE (Treatment): 0.7248\n",
"INFO:causalml: sMAPE (Control): 0.5474\n",
"INFO:causalml: sMAPE (Treatment): 0.1538\n",
"INFO:causalml: Gini (Control): 0.7380\n",
"INFO:causalml: Gini (Treatment): 0.9949\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0995\n",
"INFO:causalml: RMSE (Treatment): 0.7069\n",
"INFO:causalml: sMAPE (Control): 0.5420\n",
"INFO:causalml: sMAPE (Treatment): 0.1515\n",
"INFO:causalml: Gini (Control): 0.7201\n",
"INFO:causalml: Gini (Treatment): 0.9951\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0859\n",
"INFO:causalml: RMSE (Treatment): 0.7073\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"228 -0.479531 0.804877 -0.334062 0.447143 0.576800 -0.277841 -0.766331 \n",
"866 -2.492060 0.007731 -0.940262 -0.496057 1.104068 0.043952 0.520345 \n",
"851 -1.580498 2.916707 -1.305058 0.877563 0.733961 -1.876975 2.623342 \n",
"795 -1.189871 1.612262 -1.396546 -0.154045 0.178844 0.278181 -0.852625 \n",
"241 -0.810324 2.347069 -0.803031 1.814552 0.121529 -2.824841 -0.307345 \n",
".. ... ... ... ... ... ... ... \n",
"583 -0.820218 -0.945137 -1.568877 -0.082810 0.614237 -0.346996 0.919443 \n",
"74 -0.996694 0.160173 -2.637286 -2.023282 1.303478 -1.990375 1.207325 \n",
"453 -1.249839 -0.644978 -0.359153 1.084902 -0.516596 -0.365041 -0.139304 \n",
"723 -0.107996 1.393754 0.825508 0.735281 -0.586520 -0.348836 0.538970 \n",
"874 0.534013 2.308714 0.793617 1.992239 -1.610429 -0.414126 1.030813 \n",
"\n",
" X0 X1 \n",
"228 -0.766331 -0.277841 \n",
"866 0.520345 0.043952 \n",
"851 2.623342 -1.876975 \n",
"795 -0.852625 0.278181 \n",
"241 -0.307345 -2.824841 \n",
".. ... ... \n",
"583 0.919443 -0.346996 \n",
"74 1.207325 -1.990375 \n",
"453 -0.139304 -0.365041 \n",
"723 0.538970 -0.348836 \n",
"874 1.030813 -0.414126 \n",
"\n",
"[800 rows x 9 columns], 'y': 228 8.255998\n",
"866 6.414822\n",
"851 18.155148\n",
"795 5.028396\n",
"241 7.793042\n",
" ... \n",
"583 -2.819370\n",
"74 8.688916\n",
"453 4.442757\n",
"723 12.592456\n",
"874 16.052226\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 228 True\n",
"866 True\n",
"851 True\n",
"795 True\n",
"241 True\n",
" ... \n",
"583 False\n",
"74 True\n",
"453 True\n",
"723 True\n",
"874 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"451 -0.565758 1.128663 -0.324990 -0.337899 -0.015063 0.346414 0.109342 \n",
"945 0.033044 1.015786 0.124006 -0.242485 -0.539339 -2.297596 1.009465 \n",
"145 -2.815317 1.548436 -0.624088 0.844806 0.438846 0.070108 -0.126092 \n",
"777 -0.500132 1.841908 0.773237 -0.146416 -0.489405 -1.162506 1.188357 \n",
"422 -1.830252 0.894077 -0.267716 -0.367326 2.202751 -0.079619 0.119763 \n",
".. ... ... ... ... ... ... ... \n",
"293 -2.019915 -0.918739 -1.767926 -1.403425 -0.740373 -1.576824 0.279021 \n",
"84 -1.231904 0.946563 -0.520574 -1.241545 -1.073605 0.279123 0.201559 \n",
"549 1.093852 0.562148 -1.545915 -0.970186 -0.027322 0.044818 1.075594 \n",
"315 -0.753510 0.301264 1.390165 1.628534 0.274823 -0.437217 0.126621 \n",
"426 -0.479176 1.237188 -0.726685 -0.314696 0.767152 -0.740509 -0.373064 \n",
"\n",
" X0 X1 \n",
"451 0.109342 0.346414 \n",
"945 1.009465 -2.297596 \n",
"145 -0.126092 0.070108 \n",
"777 1.188357 -1.162506 \n",
"422 0.119763 -0.079619 \n",
".. ... ... \n",
"293 0.279021 -1.576824 \n",
"84 0.201559 0.279123 \n",
"549 1.075594 0.044818 \n",
"315 0.126621 -0.437217 \n",
"426 -0.373064 -0.740509 \n",
"\n",
"[800 rows x 9 columns], 'y': 451 9.760052\n",
"945 10.461920\n",
"145 -4.715614\n",
"777 12.450890\n",
"422 11.141529\n",
" ... \n",
"293 -11.609205\n",
"84 -7.210816\n",
"549 15.679880\n",
"315 11.545213\n",
"426 8.947595\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 451 True\n",
"945 True\n",
"145 False\n",
"777 True\n",
"422 True\n",
" ... \n",
"293 False\n",
"84 False\n",
"549 True\n",
"315 True\n",
"426 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"174 -1.209450 2.573548 -1.875041 0.466606 -2.593907 -0.787080 0.157327 \n",
"660 -1.323851 1.441421 0.356298 -1.619747 1.211768 -0.134022 0.702311 \n",
"480 -0.377755 0.312070 0.399832 1.532559 0.348411 0.225440 0.753623 \n",
"919 0.927871 1.119628 -0.747494 -0.682620 0.435117 -2.887356 0.670618 \n",
"774 -0.770624 0.071105 0.522571 -0.979329 0.197455 -1.304148 0.066355 \n",
".. ... ... ... ... ... ... ... \n",
"875 -2.411078 0.159398 0.119580 0.748787 -0.457547 0.313736 -0.560625 \n",
"568 0.513430 0.522040 0.017360 -0.306262 -0.660055 -1.021097 1.297397 \n",
"375 -1.851590 -0.485257 -0.505663 -0.368564 -1.023961 -1.380998 1.320727 \n",
"106 -1.469398 1.456738 0.120477 -0.396731 -2.091289 -1.960327 0.856698 \n",
"713 -0.587834 2.036860 -1.408772 1.303268 -0.384541 -0.825095 -0.057160 \n",
"\n",
" X0 X1 \n",
"174 0.157327 -0.787080 \n",
"660 0.702311 -0.134022 \n",
"480 0.753623 0.225440 \n",
"919 0.670618 -2.887356 \n",
"774 0.066355 -1.304148 \n",
".. ... ... \n",
"875 -0.560625 0.313736 \n",
"568 1.297397 -1.021097 \n",
"375 1.320727 -1.380998 \n",
"106 0.856698 -1.960327 \n",
"713 -0.057160 -0.825095 \n",
"\n",
"[800 rows x 9 columns], 'y': 174 1.619585\n",
"660 11.491007\n",
"480 15.267030\n",
"919 12.794015\n",
"774 6.080336\n",
" ... \n",
"875 1.205333\n",
"568 13.469854\n",
"375 3.740574\n",
"106 -8.631314\n",
"713 9.135870\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 174 True\n",
"660 True\n",
"480 True\n",
"919 True\n",
"774 True\n",
" ... \n",
"875 True\n",
"568 True\n",
"375 True\n",
"106 False\n",
"713 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Control): 0.5643\n",
"INFO:causalml: sMAPE (Treatment): 0.1448\n",
"INFO:causalml: Gini (Control): 0.7575\n",
"INFO:causalml: Gini (Treatment): 0.9950\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0283\n",
"INFO:causalml: RMSE (Treatment): 0.7092\n",
"INFO:causalml: sMAPE (Control): 0.5471\n",
"INFO:causalml: sMAPE (Treatment): 0.1393\n",
"INFO:causalml: Gini (Control): 0.7309\n",
"INFO:causalml: Gini (Treatment): 0.9946\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0752\n",
"INFO:causalml: RMSE (Treatment): 0.7231\n",
"INFO:causalml: sMAPE (Control): 0.5220\n",
"INFO:causalml: sMAPE (Treatment): 0.1459\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"275 -2.120068 -0.318202 0.171676 1.909965 -0.392065 -1.480635 -1.148364 \n",
"941 -0.858300 2.689664 0.060630 -0.800292 -0.649474 -0.542851 0.680252 \n",
"637 -1.283359 0.832725 0.002037 -1.432698 -2.375726 -1.559718 1.057731 \n",
"132 -4.155165 1.496680 -0.815922 1.278145 -1.973326 0.148467 0.226076 \n",
"667 -2.198198 0.869380 0.049001 1.338380 -0.046996 0.313350 1.524353 \n",
".. ... ... ... ... ... ... ... \n",
"775 -2.360643 1.573135 -1.344099 0.823569 -1.137799 -0.227823 0.357754 \n",
"53 -2.930083 0.879228 -0.579348 0.759465 -0.462352 -2.221874 2.337682 \n",
"208 -1.829170 0.787526 -0.881231 0.083780 -1.917817 -2.457417 0.754683 \n",
"157 -1.514941 0.601247 -2.511296 0.377276 0.269567 -0.229923 0.816437 \n",
"404 0.269959 -1.426995 -0.624877 1.207414 -0.632554 0.826927 -0.342909 \n",
"\n",
" X0 X1 \n",
"275 -1.148364 -1.480635 \n",
"941 0.680252 -0.542851 \n",
"637 1.057731 -1.559718 \n",
"132 0.226076 0.148467 \n",
"667 1.524353 0.313350 \n",
".. ... ... \n",
"775 0.357754 -0.227823 \n",
"53 2.337682 -2.221874 \n",
"208 0.754683 -2.457417 \n",
"157 0.816437 -0.229923 \n",
"404 -0.342909 0.826927 \n",
"\n",
"[800 rows x 9 columns], 'y': 275 -1.163599\n",
"941 9.630585\n",
"637 -10.850731\n",
"132 -3.629107\n",
"667 11.985876\n",
" ... \n",
"775 -7.934090\n",
"53 7.558222\n",
"208 -9.928296\n",
"157 8.671216\n",
"404 -1.323791\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 275 True\n",
"941 True\n",
"637 False\n",
"132 True\n",
"667 True\n",
" ... \n",
"775 False\n",
"53 True\n",
"208 False\n",
"157 True\n",
"404 False\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"49 -0.982952 2.225363 -1.526931 -0.694241 0.029243 -0.455032 0.234971 \n",
"682 -0.223833 1.310405 -0.330832 0.125670 1.368049 0.141310 -0.773242 \n",
"280 -0.924446 -0.495998 -1.067446 -0.475160 -0.560881 -1.593906 1.057462 \n",
"220 0.757100 1.090061 -0.251709 0.571761 -1.084509 -1.752894 0.512210 \n",
"862 -2.626160 -0.085283 0.861203 0.598807 0.007442 -1.089904 -0.141363 \n",
".. ... ... ... ... ... ... ... \n",
"269 0.573973 -0.053532 -0.674589 -3.004365 1.021180 -0.146757 0.753673 \n",
"194 -1.962419 0.749901 -0.124856 1.434591 -0.397338 -0.555662 0.763135 \n",
"555 -0.854260 -0.912091 -1.765080 1.636757 -1.353961 -2.104020 1.321314 \n",
"987 -0.812298 1.417451 -0.621462 -0.360324 -1.103340 -1.337427 0.384151 \n",
"893 -1.491824 0.452183 -1.205184 0.340149 1.739224 -0.632809 0.210046 \n",
"\n",
" X0 X1 \n",
"49 0.234971 -0.455032 \n",
"682 -0.773242 0.141310 \n",
"280 1.057462 -1.593906 \n",
"220 0.512210 -1.752894 \n",
"862 -0.141363 -1.089904 \n",
".. ... ... \n",
"269 0.753673 -0.146757 \n",
"194 0.763135 -0.555662 \n",
"555 1.321314 -2.104020 \n",
"987 0.384151 -1.337427 \n",
"893 0.210046 -0.632809 \n",
"\n",
"[800 rows x 9 columns], 'y': 49 8.274390\n",
"682 11.702839\n",
"280 5.994137\n",
"220 10.878908\n",
"862 1.585107\n",
" ... \n",
"269 0.249321\n",
"194 7.805228\n",
"555 -6.061451\n",
"987 -4.527449\n",
"893 10.444157\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 49 True\n",
"682 True\n",
"280 True\n",
"220 True\n",
"862 True\n",
" ... \n",
"269 False\n",
"194 True\n",
"555 False\n",
"987 False\n",
"893 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Control): 0.7268\n",
"INFO:causalml: Gini (Treatment): 0.9948\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1012\n",
"INFO:causalml: RMSE (Treatment): 0.6904\n",
"INFO:causalml: sMAPE (Control): 0.5517\n",
"INFO:causalml: sMAPE (Treatment): 0.1317\n",
"INFO:causalml: Gini (Control): 0.7215\n",
"INFO:causalml: Gini (Treatment): 0.9953\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0529\n",
"INFO:causalml: RMSE (Treatment): 0.6916\n",
"INFO:causalml: sMAPE (Control): 0.5471\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"965 -0.258222 2.196243 -1.509052 0.242610 -0.852991 -0.125788 3.020499 \n",
"18 -1.721390 0.991295 -1.008932 1.539819 -0.923055 -0.609885 -0.196033 \n",
"816 -0.262987 0.921280 -0.389069 -0.883456 -2.158824 -1.767588 -0.312548 \n",
"373 -0.576617 0.832892 -0.252974 2.508469 -0.393241 0.636110 0.102158 \n",
"39 -0.961481 1.275170 -1.485334 -0.763943 -1.116911 0.131391 1.712740 \n",
".. ... ... ... ... ... ... ... \n",
"127 -1.184442 1.633850 -0.489806 0.806896 -0.654125 -0.324203 -1.297638 \n",
"798 0.298268 1.287738 -0.339625 0.484676 -1.361646 -0.844488 0.584065 \n",
"240 -1.161647 1.005936 0.248576 1.135152 -0.087861 -0.969873 -0.362578 \n",
"319 -0.246796 1.042340 0.473929 -1.882334 -1.252649 -0.129804 -2.165228 \n",
"86 -1.171030 0.999708 -0.474575 -0.002353 1.214732 -0.432952 -1.419492 \n",
"\n",
" X0 X1 \n",
"965 3.020499 -0.125788 \n",
"18 -0.196033 -0.609885 \n",
"816 -0.312548 -1.767588 \n",
"373 0.102158 0.636110 \n",
"39 1.712740 0.131391 \n",
".. ... ... \n",
"127 -1.297638 -0.324203 \n",
"798 0.584065 -0.844488 \n",
"240 -0.362578 -0.969873 \n",
"319 -2.165228 -0.129804 \n",
"86 -1.419492 -0.432952 \n",
"\n",
"[800 rows x 9 columns], 'y': 965 19.735711\n",
"18 -5.046192\n",
"816 -0.020121\n",
"373 12.268099\n",
"39 -6.109185\n",
" ... \n",
"127 2.218793\n",
"798 10.206839\n",
"240 6.512254\n",
"319 -2.951778\n",
"86 4.893758\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 965 True\n",
"18 False\n",
"816 True\n",
"373 True\n",
"39 False\n",
" ... \n",
"127 True\n",
"798 True\n",
"240 True\n",
"319 True\n",
"86 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"355 -0.326494 2.433717 0.080853 1.915810 -0.164674 0.466589 -0.152236 \n",
"586 -1.822525 1.328268 -0.580694 0.434084 -1.937340 0.184012 3.008666 \n",
"116 -1.086365 -0.990762 0.079434 1.325440 1.224208 0.738869 0.436850 \n",
"631 0.271998 2.338601 -1.112341 1.567964 -0.208038 -0.964256 0.333245 \n",
"94 -0.994409 -0.051791 -0.396042 0.809534 0.517539 -0.681357 0.730109 \n",
".. ... ... ... ... ... ... ... \n",
"227 -0.757316 2.009235 -0.832385 1.297304 -0.017571 -1.484499 0.073045 \n",
"643 -0.837543 2.690282 -0.956587 1.519004 -3.148478 1.188059 1.128666 \n",
"834 -0.921188 0.601325 -1.805151 0.607381 0.656701 0.552350 0.119174 \n",
"646 -0.626251 1.021268 0.186793 1.691995 -1.534219 -0.559809 0.720521 \n",
"530 -1.096231 -0.378584 -1.082200 0.789542 -0.662083 -1.309586 1.838881 \n",
"\n",
" X0 X1 \n",
"355 -0.152236 0.466589 \n",
"586 3.008666 0.184012 \n",
"116 0.436850 0.738869 \n",
"631 0.333245 -0.964256 \n",
"94 0.730109 -0.681357 \n",
".. ... ... \n",
"227 0.073045 -1.484499 \n",
"643 1.128666 1.188059 \n",
"834 0.119174 0.552350 \n",
"646 0.720521 -0.559809 \n",
"530 1.838881 -1.309586 \n",
"\n",
"[800 rows x 9 columns], 'y': 355 13.712619\n",
"586 -8.767996\n",
"116 13.066025\n",
"631 14.220450\n",
"94 10.995359\n",
" ... \n",
"227 9.555874\n",
"643 9.118838\n",
"834 10.523346\n",
"646 9.399353\n",
"530 10.101463\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 355 True\n",
"586 False\n",
"116 True\n",
"631 True\n",
"94 True\n",
" ... \n",
"227 True\n",
"643 True\n",
"834 True\n",
"646 True\n",
"530 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Treatment): 0.1404\n",
"INFO:causalml: Gini (Control): 0.7361\n",
"INFO:causalml: Gini (Treatment): 0.9952\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0703\n",
"INFO:causalml: RMSE (Treatment): 0.6898\n",
"INFO:causalml: sMAPE (Control): 0.5459\n",
"INFO:causalml: sMAPE (Treatment): 0.1425\n",
"INFO:causalml: Gini (Control): 0.7482\n",
"INFO:causalml: Gini (Treatment): 0.9953\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1016\n",
"INFO:causalml: RMSE (Treatment): 0.7318\n",
"INFO:causalml: sMAPE (Control): 0.5510\n",
"INFO:causalml: sMAPE (Treatment): 0.1584\n",
"INFO:causalml: Gini (Control): 0.7415\n",
"INFO:causalml: Gini (Treatment): 0.9951\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"705 0.261741 3.188563 -2.010226 -0.517940 1.029141 0.040541 0.750995 \n",
"44 0.360294 0.124152 -2.197239 -1.328035 -0.866502 -0.042365 0.714713 \n",
"738 -0.532602 1.680468 -1.570149 2.071328 -0.883423 -2.507779 0.510461 \n",
"359 -1.250892 0.608337 -1.159236 1.196291 -2.448155 -1.418204 2.171817 \n",
"531 -1.304303 -0.065667 -1.069981 0.533144 1.279208 -0.691485 -0.259040 \n",
".. ... ... ... ... ... ... ... \n",
"275 -2.120068 -0.318202 0.171676 1.909965 -0.392065 -1.480635 -1.148364 \n",
"128 -0.446814 1.132974 -0.280832 -0.265640 0.705652 0.138788 1.457893 \n",
"633 -2.030034 -0.174624 -0.342485 0.323698 -1.221663 0.242803 -0.377460 \n",
"502 -1.817353 2.122041 -2.913312 1.536276 -0.699921 -0.900995 1.371631 \n",
"971 -1.011609 0.842579 -1.257641 1.094270 0.446977 1.836771 1.109406 \n",
"\n",
" X0 X1 \n",
"705 0.750995 0.040541 \n",
"44 0.714713 -0.042365 \n",
"738 0.510461 -2.507779 \n",
"359 2.171817 -1.418204 \n",
"531 -0.259040 -0.691485 \n",
".. ... ... \n",
"275 -1.148364 -1.480635 \n",
"128 1.457893 0.138788 \n",
"633 -0.377460 0.242803 \n",
"502 1.371631 -0.900995 \n",
"971 1.109406 1.836771 \n",
"\n",
"[800 rows x 9 columns], 'y': 705 18.039614\n",
"44 8.606075\n",
"738 8.470271\n",
"359 7.556557\n",
"531 7.735845\n",
" ... \n",
"275 -1.163599\n",
"128 16.791480\n",
"633 -0.246762\n",
"502 9.319528\n",
"971 16.046613\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 705 True\n",
"44 True\n",
"738 True\n",
"359 True\n",
"531 True\n",
" ... \n",
"275 True\n",
"128 True\n",
"633 True\n",
"502 True\n",
"971 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"75 -0.681840 -0.860902 -0.003540 0.004867 -0.711607 1.445133 2.210988 \n",
"791 -0.302223 -0.035221 -0.779010 -1.067815 -1.088018 1.298864 1.837893 \n",
"668 -0.124828 0.334842 0.337000 -0.098033 -0.948690 -0.993841 1.156376 \n",
"861 -2.010612 -1.734882 -0.315201 0.998088 0.426792 -0.740070 0.178178 \n",
"708 -0.500479 1.096561 0.068238 -0.285794 -0.225941 -1.796636 1.564528 \n",
".. ... ... ... ... ... ... ... \n",
"491 0.047326 0.391397 -0.404472 0.060057 0.642744 -0.800913 -0.652640 \n",
"8 -1.192098 0.543069 0.448059 1.496816 1.148883 -0.101334 0.352319 \n",
"332 -1.250673 0.380127 -0.875099 1.332656 0.562342 0.345025 1.790799 \n",
"202 -1.442253 1.720388 1.836666 1.221145 -0.572915 -1.254973 -1.342614 \n",
"890 -1.222633 2.135828 -0.734609 -0.488569 -0.600416 -0.479990 -0.460961 \n",
"\n",
" X0 X1 \n",
"75 2.210988 1.445133 \n",
"791 1.837893 1.298864 \n",
"668 1.156376 -0.993841 \n",
"861 0.178178 -0.740070 \n",
"708 1.564528 -1.796636 \n",
".. ... ... \n",
"491 -0.652640 -0.800913 \n",
"8 0.352319 -0.101334 \n",
"332 1.790799 0.345025 \n",
"202 -1.342614 -1.254973 \n",
"890 -0.460961 -0.479990 \n",
"\n",
"[800 rows x 9 columns], 'y': 75 -4.842126\n",
"791 12.700214\n",
"668 10.579344\n",
"861 4.083148\n",
"708 12.369009\n",
" ... \n",
"491 8.770425\n",
"8 13.374297\n",
"332 16.296656\n",
"202 2.297590\n",
"890 -3.809211\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 75 False\n",
"791 True\n",
"668 True\n",
"861 True\n",
"708 True\n",
" ... \n",
"491 True\n",
"8 True\n",
"332 True\n",
"202 True\n",
"890 False\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9884\n",
"INFO:causalml: RMSE (Treatment): 0.6927\n",
"INFO:causalml: sMAPE (Control): 0.5061\n",
"INFO:causalml: sMAPE (Treatment): 0.1410\n",
"INFO:causalml: Gini (Control): 0.7191\n",
"INFO:causalml: Gini (Treatment): 0.9945\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9921\n",
"INFO:causalml: RMSE (Treatment): 0.7493\n",
"INFO:causalml: sMAPE (Control): 0.5346\n",
"INFO:causalml: sMAPE (Treatment): 0.1501\n",
"INFO:causalml: Gini (Control): 0.7535\n",
"INFO:causalml: Gini (Treatment): 0.9943\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0065\n",
"INFO:causalml: RMSE (Treatment): 0.6871\n",
"INFO:causalml: sMAPE (Control): 0.5569\n",
"INFO:causalml: sMAPE (Treatment): 0.1468\n",
"INFO:causalml: Gini (Control): 0.7082\n",
"INFO:causalml: Gini (Treatment): 0.9952\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"84 -1.231904 0.946563 -0.520574 -1.241545 -1.073605 0.279123 0.201559 \n",
"920 -2.817866 0.619407 -1.383961 -0.429572 0.136050 -0.313979 1.711518 \n",
"406 -1.787177 0.601264 -0.241539 -0.288932 0.204519 1.070860 0.778961 \n",
"841 -0.646074 -0.355693 -1.037555 1.155296 -0.106020 -1.428374 0.724408 \n",
"700 -1.952536 -0.329336 -1.215430 0.057645 -0.943685 -0.856869 2.159186 \n",
".. ... ... ... ... ... ... ... \n",
"88 0.116208 0.595981 -0.004589 2.371107 0.664072 1.005779 0.675959 \n",
"703 -0.880997 -1.820627 0.171736 -0.167854 0.599802 -2.011412 -0.082251 \n",
"787 0.106762 2.948496 -1.895033 -1.510659 -0.813685 -0.585402 -0.112157 \n",
"565 0.489147 1.219821 -1.070019 0.305099 0.624808 -0.830622 1.045480 \n",
"672 -1.285233 1.492835 -0.461824 -1.224488 -2.543523 -0.472754 1.225212 \n",
"\n",
" X0 X1 \n",
"84 0.201559 0.279123 \n",
"920 1.711518 -0.313979 \n",
"406 0.778961 1.070860 \n",
"841 0.724408 -1.428374 \n",
"700 2.159186 -0.856869 \n",
".. ... ... \n",
"88 0.675959 1.005779 \n",
"703 -0.082251 -2.011412 \n",
"787 -0.112157 -0.585402 \n",
"565 1.045480 -0.830622 \n",
"672 1.225212 -0.472754 \n",
"\n",
"[800 rows x 9 columns], 'y': 84 -7.210816\n",
"920 -8.542729\n",
"406 9.585905\n",
"841 9.168319\n",
"700 -9.193834\n",
" ... \n",
"88 19.338740\n",
"703 -3.147667\n",
"787 7.397373\n",
"565 17.014565\n",
"672 3.326896\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 84 False\n",
"920 False\n",
"406 True\n",
"841 True\n",
"700 False\n",
" ... \n",
"88 True\n",
"703 False\n",
"787 True\n",
"565 True\n",
"672 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"597 -0.321245 0.240069 -1.490242 2.231212 -0.904581 0.578349 -0.009237 \n",
"904 -1.109443 3.107200 -0.951024 -0.079768 1.948789 -1.132518 -0.890302 \n",
"290 0.299643 -1.028151 0.230684 -0.517070 0.924262 -1.450635 0.759954 \n",
"909 -0.448326 1.299826 0.401639 -0.500486 -1.006631 -0.906303 1.442992 \n",
"875 -2.411078 0.159398 0.119580 0.748787 -0.457547 0.313736 -0.560625 \n",
".. ... ... ... ... ... ... ... \n",
"453 -1.249839 -0.644978 -0.359153 1.084902 -0.516596 -0.365041 -0.139304 \n",
"430 -0.536437 -0.421504 -0.788914 -1.566788 -0.855747 -0.483673 0.158809 \n",
"116 -1.086365 -0.990762 0.079434 1.325440 1.224208 0.738869 0.436850 \n",
"634 -0.085908 0.582399 -1.871995 -0.128561 1.399274 -2.013647 -0.399001 \n",
"734 -2.534543 1.297053 -0.227526 -0.605377 -1.703765 -0.280342 0.415044 \n",
"\n",
" X0 X1 \n",
"597 -0.009237 0.578349 \n",
"904 -0.890302 -1.132518 \n",
"290 0.759954 -1.450635 \n",
"909 1.442992 -0.906303 \n",
"875 -0.560625 0.313736 \n",
".. ... ... \n",
"453 -0.139304 -0.365041 \n",
"430 0.158809 -0.483673 \n",
"116 0.436850 0.738869 \n",
"634 -0.399001 -2.013647 \n",
"734 0.415044 -0.280342 \n",
"\n",
"[800 rows x 9 columns], 'y': 597 -1.143963\n",
"904 10.065464\n",
"290 12.664366\n",
"909 11.235374\n",
"875 1.205333\n",
" ... \n",
"453 4.442757\n",
"430 3.344323\n",
"116 13.066025\n",
"634 8.990907\n",
"734 -0.213830\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 597 False\n",
"904 True\n",
"290 True\n",
"909 True\n",
"875 True\n",
" ... \n",
"453 True\n",
"430 True\n",
"116 True\n",
"634 True\n",
"734 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"90 0.097345 0.533868 -1.112249 0.999274 0.230117 -1.125698 0.717416 \n",
"686 -1.377233 -0.617476 -0.353271 2.000711 -1.831548 0.632213 0.262429 \n",
"240 -1.161647 1.005936 0.248576 1.135152 -0.087861 -0.969873 -0.362578 \n",
"511 0.263820 1.872437 -0.939853 1.755917 -1.252826 1.759191 0.714215 \n",
"622 -0.848690 -0.845794 0.267025 -0.137135 0.356153 -0.001756 0.376872 \n",
".. ... ... ... ... ... ... ... \n",
"671 -1.805567 1.966280 0.355705 0.675031 -1.531579 -0.217720 -0.701737 \n",
"964 0.753382 0.592423 -0.618551 0.045706 -0.493502 -1.581298 -1.715736 \n",
"299 -1.370319 0.104371 -0.773003 1.741095 0.119556 -1.227923 -0.272634 \n",
"572 -2.548463 -0.098732 -0.957317 0.639523 -0.249403 1.104659 -0.047994 \n",
"754 -1.008916 0.089333 -0.381037 1.182553 0.653293 -0.475920 0.036262 \n",
"\n",
" X0 X1 \n",
"90 0.717416 -1.125698 \n",
"686 0.262429 0.632213 \n",
"240 -0.362578 -0.969873 \n",
"511 0.714215 1.759191 \n",
"622 0.376872 -0.001756 \n",
".. ... ... \n",
"671 -0.701737 -0.217720 \n",
"964 -1.715736 -1.581298 \n",
"299 -0.272634 -1.227923 \n",
"572 -0.047994 1.104659 \n",
"754 0.036262 -0.475920 \n",
"\n",
"[800 rows x 9 columns], 'y': 90 13.341932\n",
"686 4.432389\n",
"240 6.512254\n",
"511 15.928076\n",
"622 8.845133\n",
" ... \n",
"671 -6.150284\n",
"964 3.134159\n",
"299 5.614664\n",
"572 -8.024569\n",
"754 9.673575\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 90 True\n",
"686 True\n",
"240 True\n",
"511 True\n",
"622 True\n",
" ... \n",
"671 False\n",
"964 True\n",
"299 True\n",
"572 False\n",
"754 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0987\n",
"INFO:causalml: RMSE (Treatment): 0.7208\n",
"INFO:causalml: sMAPE (Control): 0.5237\n",
"INFO:causalml: sMAPE (Treatment): 0.1429\n",
"INFO:causalml: Gini (Control): 0.7470\n",
"INFO:causalml: Gini (Treatment): 0.9951\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0540\n",
"INFO:causalml: RMSE (Treatment): 0.7624\n",
"INFO:causalml: sMAPE (Control): 0.5464\n",
"INFO:causalml: sMAPE (Treatment): 0.1583\n",
"INFO:causalml: Gini (Control): 0.7329\n",
"INFO:causalml: Gini (Treatment): 0.9945\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1489\n",
"INFO:causalml: RMSE (Treatment): 0.7569\n",
"INFO:causalml: sMAPE (Control): 0.5456\n",
"INFO:causalml: sMAPE (Treatment): 0.1493\n",
"INFO:causalml: Gini (Control): 0.7273\n",
"INFO:causalml: Gini (Treatment): 0.9940\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"334 -0.156694 0.911286 0.765684 0.401905 -1.416657 -2.667282 -1.314160 \n",
"262 -1.630045 -1.251623 -0.321312 1.248217 0.588834 -0.498320 0.369726 \n",
"674 -1.388549 1.485270 -0.646514 -0.090141 1.767213 0.077538 -0.650185 \n",
"955 -0.372062 0.536404 -1.497404 1.331291 1.949449 -0.806243 1.113560 \n",
"832 -0.682884 1.926730 -0.161279 -0.512041 -0.170792 -1.586715 0.243828 \n",
".. ... ... ... ... ... ... ... \n",
"28 0.355363 -1.712344 -0.541527 -1.500008 -0.178104 -0.523930 -0.313656 \n",
"582 -1.856578 1.519025 -0.354817 1.777607 -0.103456 -0.540906 0.894554 \n",
"294 0.718662 0.293405 -0.329930 0.322284 -0.725980 -1.672422 0.046675 \n",
"846 -1.672185 -0.347375 0.787428 0.336686 0.622999 0.804101 1.251836 \n",
"716 -1.737178 1.386498 -1.564507 -1.024549 1.582414 -0.183054 0.305977 \n",
"\n",
" X0 X1 \n",
"334 -1.314160 -2.667282 \n",
"262 0.369726 -0.498320 \n",
"674 -0.650185 0.077538 \n",
"955 1.113560 -0.806243 \n",
"832 0.243828 -1.586715 \n",
".. ... ... \n",
"28 -0.313656 -0.523930 \n",
"582 0.894554 -0.540906 \n",
"294 0.046675 -1.672422 \n",
"846 1.251836 0.804101 \n",
"716 0.305977 -0.183054 \n",
"\n",
"[800 rows x 9 columns], 'y': 334 -0.271330\n",
"262 7.461448\n",
"674 9.459822\n",
"955 18.503792\n",
"832 7.911675\n",
" ... \n",
"28 4.753158\n",
"582 10.549091\n",
"294 8.885443\n",
"846 12.705539\n",
"716 9.358799\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 334 True\n",
"262 True\n",
"674 True\n",
"955 True\n",
"832 True\n",
" ... \n",
"28 True\n",
"582 True\n",
"294 True\n",
"846 True\n",
"716 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"475 -1.371406 -1.789706 -1.298206 -1.611236 -0.709442 -0.195347 2.724470 \n",
"588 -0.930255 -0.378142 0.632255 0.279449 -1.818598 -1.226886 0.208515 \n",
"279 -2.042622 -0.483061 -0.027763 0.310754 0.006903 0.717223 1.415660 \n",
"206 0.034341 -1.189055 -0.976649 0.149847 1.575362 -0.130203 2.269800 \n",
"952 -0.623857 0.509187 -1.613917 -0.527539 -0.021147 -1.638291 1.771485 \n",
".. ... ... ... ... ... ... ... \n",
"343 -1.245795 1.748387 0.828184 -1.775403 -0.972468 -0.866709 0.489782 \n",
"843 -1.819689 2.321605 1.205835 1.378205 -0.687589 -1.421073 -0.008063 \n",
"469 -1.955975 0.090817 0.294803 -2.001217 -0.945376 -1.628086 1.883115 \n",
"369 -2.390398 0.327099 -0.741555 -1.805522 -0.865166 -1.456077 -2.343644 \n",
"111 1.024268 0.556637 -0.514663 1.858435 -0.414872 -0.567619 0.793652 \n",
"\n",
" X0 X1 \n",
"475 2.724470 -0.195347 \n",
"588 0.208515 -1.226886 \n",
"279 1.415660 0.717223 \n",
"206 2.269800 -0.130203 \n",
"952 1.771485 -1.638291 \n",
".. ... ... \n",
"343 0.489782 -0.866709 \n",
"843 -0.008063 -1.421073 \n",
"469 1.883115 -1.628086 \n",
"369 -2.343644 -1.456077 \n",
"111 0.793652 -0.567619 \n",
"\n",
"[800 rows x 9 columns], 'y': 475 -10.586297\n",
"588 -7.223205\n",
"279 -6.155145\n",
"206 20.680223\n",
"952 -2.868485\n",
" ... \n",
"343 4.731928\n",
"843 6.032900\n",
"469 4.435916\n",
"369 -11.558437\n",
"111 16.735009\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 475 False\n",
"588 False\n",
"279 False\n",
"206 True\n",
"952 False\n",
" ... \n",
"343 True\n",
"843 True\n",
"469 True\n",
"369 False\n",
"111 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"899 -0.395984 -0.753509 -0.320954 -0.871588 -1.254406 -0.867205 0.087498 \n",
"34 0.276106 2.330464 0.579228 -0.077358 0.414878 1.933343 1.404165 \n",
"761 -1.783477 -0.235446 -0.829345 -1.351781 0.988020 -0.599609 1.020585 \n",
"699 -1.175823 0.350680 0.033996 0.794509 -0.475092 -2.358432 0.259472 \n",
"546 -2.888766 0.186649 0.030494 0.288355 -1.192114 -0.335307 1.980589 \n",
".. ... ... ... ... ... ... ... \n",
"72 -2.020604 -1.051226 -0.996921 -0.824436 -1.488663 -0.206553 0.489298 \n",
"30 -1.029675 -0.036475 0.168119 0.799758 0.713907 -1.117296 0.492739 \n",
"576 -0.258472 1.109831 0.044998 0.470214 0.134336 -1.293955 1.695302 \n",
"678 0.508374 0.644654 -0.261807 0.486776 -1.449243 0.145155 1.107169 \n",
"669 -0.015285 1.132134 -0.997982 0.382392 0.634601 -2.152286 -0.881791 \n",
"\n",
" X0 X1 \n",
"899 0.087498 -0.867205 \n",
"34 1.404165 1.933343 \n",
"761 1.020585 -0.599609 \n",
"699 0.259472 -2.358432 \n",
"546 1.980589 -0.335307 \n",
".. ... ... \n",
"72 0.489298 -0.206553 \n",
"30 0.492739 -1.117296 \n",
"576 1.695302 -1.293955 \n",
"678 1.107169 0.145155 \n",
"669 -0.881791 -2.152286 \n",
"\n",
"[800 rows x 9 columns], 'y': 899 2.709571\n",
"34 22.166660\n",
"761 7.959767\n",
"699 4.759513\n",
"546 -11.053337\n",
" ... \n",
"72 -12.586391\n",
"30 10.313749\n",
"576 16.021321\n",
"678 13.061989\n",
"669 6.961904\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 899 True\n",
"34 True\n",
"761 True\n",
"699 True\n",
"546 False\n",
" ... \n",
"72 False\n",
"30 True\n",
"576 True\n",
"678 True\n",
"669 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0130\n",
"INFO:causalml: RMSE (Treatment): 0.7012\n",
"INFO:causalml: sMAPE (Control): 0.5313\n",
"INFO:causalml: sMAPE (Treatment): 0.1381\n",
"INFO:causalml: Gini (Control): 0.7394\n",
"INFO:causalml: Gini (Treatment): 0.9951\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"946 -0.056748 1.082737 0.733627 1.643594 -0.787399 -1.668734 1.968422 \n",
"838 -0.753922 -0.045816 -0.873461 1.047785 -0.824178 -1.028226 -0.543500 \n",
"677 -0.926078 0.166307 -2.183101 1.988610 -0.286812 -1.952046 0.131363 \n",
"805 -2.622025 -0.308385 -0.881486 -0.183280 0.289018 -2.986494 0.245462 \n",
"75 -0.681840 -0.860902 -0.003540 0.004867 -0.711607 1.445133 2.210988 \n",
".. ... ... ... ... ... ... ... \n",
"790 -0.783165 1.865815 -0.439194 0.520276 -0.904192 0.041655 1.027622 \n",
"410 -0.703120 0.450567 -0.504908 0.896033 -1.220663 -3.269095 -1.609582 \n",
"968 -0.626450 0.610291 -0.847638 0.077436 0.480144 -1.839511 0.703036 \n",
"615 -1.312910 0.549883 -0.743413 1.273369 -2.435562 -1.071918 2.090585 \n",
"864 -1.315606 1.131888 -1.274700 -1.261592 -1.388695 -0.667723 0.600493 \n",
"\n",
" X0 X1 \n",
"946 1.968422 -1.668734 \n",
"838 -0.543500 -1.028226 \n",
"677 0.131363 -1.952046 \n",
"805 0.245462 -2.986494 \n",
"75 2.210988 1.445133 \n",
".. ... ... \n",
"790 1.027622 0.041655 \n",
"410 -1.609582 -3.269095 \n",
"968 0.703036 -1.839511 \n",
"615 2.090585 -1.071918 \n",
"864 0.600493 -0.667723 \n",
"\n",
"[800 rows x 9 columns], 'y': 946 16.526801\n",
"838 3.145267\n",
"677 6.044637\n",
"805 -0.721618\n",
"75 -4.842126\n",
" ... \n",
"790 -2.228632\n",
"410 -3.800252\n",
"968 10.038489\n",
"615 -8.516564\n",
"864 -8.539115\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 946 True\n",
"838 True\n",
"677 True\n",
"805 True\n",
"75 False\n",
" ... \n",
"790 False\n",
"410 True\n",
"968 True\n",
"615 False\n",
"864 False\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"595 -1.371638 -0.038092 0.472554 0.208273 0.608900 -0.921483 0.132164 \n",
"488 -1.198321 1.515799 0.559743 -0.112272 -1.395849 1.313824 1.637690 \n",
"140 -0.284186 -0.187325 -1.110371 1.711081 -1.545623 0.250207 -0.110677 \n",
"318 -0.503101 -0.600895 -0.267185 0.021973 1.464131 -0.551488 -0.234670 \n",
"863 0.652126 1.026512 0.144431 0.576424 1.624713 -2.511548 1.059346 \n",
".. ... ... ... ... ... ... ... \n",
"622 -0.848690 -0.845794 0.267025 -0.137135 0.356153 -0.001756 0.376872 \n",
"601 0.106682 -0.437917 -2.078172 0.727072 -0.486981 -0.797348 0.454592 \n",
"958 -0.495343 0.847053 -0.028871 -0.718100 -0.871586 -0.630299 -0.132512 \n",
"519 -0.928551 1.643629 -0.012840 -0.089474 -1.289071 -0.902553 0.551816 \n",
"393 -1.338523 0.522130 0.208298 1.121733 -0.869824 -0.203718 -0.158286 \n",
"\n",
" X0 X1 \n",
"595 0.132164 -0.921483 \n",
"488 1.637690 1.313824 \n",
"140 -0.110677 0.250207 \n",
"318 -0.234670 -0.551488 \n",
"863 1.059346 -2.511548 \n",
".. ... ... \n",
"622 0.376872 -0.001756 \n",
"601 0.454592 -0.797348 \n",
"958 -0.132512 -0.630299 \n",
"519 0.551816 -0.902553 \n",
"393 -0.158286 -0.203718 \n",
"\n",
"[800 rows x 9 columns], 'y': 595 -1.962053\n",
"488 12.205540\n",
"140 -3.623800\n",
"318 10.068543\n",
"863 18.929095\n",
" ... \n",
"622 8.845133\n",
"601 -1.707354\n",
"958 5.051010\n",
"519 6.513308\n",
"393 -4.149152\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 595 False\n",
"488 True\n",
"140 False\n",
"318 True\n",
"863 True\n",
" ... \n",
"622 True\n",
"601 False\n",
"958 True\n",
"519 True\n",
"393 False\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Control): 2.9581\n",
"INFO:causalml: RMSE (Treatment): 0.7019\n",
"INFO:causalml: sMAPE (Control): 0.5270\n",
"INFO:causalml: sMAPE (Treatment): 0.1366\n",
"INFO:causalml: Gini (Control): 0.7395\n",
"INFO:causalml: Gini (Treatment): 0.9947\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0089\n",
"INFO:causalml: RMSE (Treatment): 0.6914\n",
"INFO:causalml: sMAPE (Control): 0.5590\n",
"INFO:causalml: sMAPE (Treatment): 0.1410\n",
"INFO:causalml: Gini (Control): 0.7382\n",
"INFO:causalml: Gini (Treatment): 0.9950\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0407\n",
"INFO:causalml: RMSE (Treatment): 0.6737\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"54 1.459268 1.943608 1.129690 0.548664 -1.340973 -0.812905 -0.963531 \n",
"785 -1.472432 -1.585313 0.723227 -2.198742 -0.861557 0.282930 -0.095028 \n",
"295 -0.983868 1.058323 -1.353941 0.550563 -1.213079 0.165130 1.300355 \n",
"411 -0.546985 0.632225 0.177670 1.152931 -1.341671 -1.043610 0.915882 \n",
"854 -2.564260 -0.151503 -0.722982 -0.358408 0.118593 -0.081853 0.348541 \n",
".. ... ... ... ... ... ... ... \n",
"346 -1.786223 0.155241 -0.006492 1.116350 0.851448 -0.701068 0.717681 \n",
"386 0.144990 2.200438 2.535124 -0.009489 -1.157723 1.477828 -0.341690 \n",
"986 -0.416562 0.868225 0.665941 0.399819 -1.513787 -1.765043 -0.200477 \n",
"428 -1.601123 -0.352920 -1.685513 2.750246 -2.723752 0.434913 0.292866 \n",
"971 -1.011609 0.842579 -1.257641 1.094270 0.446977 1.836771 1.109406 \n",
"\n",
" X0 X1 \n",
"54 -0.963531 -0.812905 \n",
"785 -0.095028 0.282930 \n",
"295 1.300355 0.165130 \n",
"411 0.915882 -1.043610 \n",
"854 0.348541 -0.081853 \n",
".. ... ... \n",
"346 0.717681 -0.701068 \n",
"386 -0.341690 1.477828 \n",
"986 -0.200477 -1.765043 \n",
"428 0.292866 0.434913 \n",
"971 1.109406 1.836771 \n",
"\n",
"[800 rows x 9 columns], 'y': 54 9.737109\n",
"785 -10.650231\n",
"295 9.933260\n",
"411 9.164325\n",
"854 -8.250436\n",
" ... \n",
"346 10.270986\n",
"386 11.776341\n",
"986 3.757036\n",
"428 1.776248\n",
"971 16.046613\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 54 True\n",
"785 False\n",
"295 True\n",
"411 True\n",
"854 False\n",
" ... \n",
"346 True\n",
"386 True\n",
"986 True\n",
"428 True\n",
"971 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"905 -1.248790 0.836818 0.276427 -0.381741 -0.785530 -1.648800 0.638075 \n",
"702 1.776446 1.312351 0.035714 -0.222481 1.693603 0.366529 0.704185 \n",
"456 -1.180741 1.678642 0.789419 1.087730 0.037308 0.563234 0.500563 \n",
"271 -1.683731 -0.490214 1.077274 1.927335 -0.447638 1.027982 -0.317068 \n",
"191 -0.155299 -0.313327 -1.913569 0.091915 -1.423443 0.136986 -0.041962 \n",
".. ... ... ... ... ... ... ... \n",
"862 -2.626160 -0.085283 0.861203 0.598807 0.007442 -1.089904 -0.141363 \n",
"499 -1.577195 -0.178122 0.207028 1.204303 -0.666172 1.145283 0.458618 \n",
"373 -0.576617 0.832892 -0.252974 2.508469 -0.393241 0.636110 0.102158 \n",
"932 -1.213129 0.643221 -0.007752 0.320722 -1.204954 0.065085 -0.764774 \n",
"657 -1.103857 0.975798 0.409175 0.707782 0.346495 -0.957502 0.942159 \n",
"\n",
" X0 X1 \n",
"905 0.638075 -1.648800 \n",
"702 0.704185 0.366529 \n",
"456 0.500563 0.563234 \n",
"271 -0.317068 1.027982 \n",
"191 -0.041962 0.136986 \n",
".. ... ... \n",
"862 -0.141363 -1.089904 \n",
"499 0.458618 1.145283 \n",
"373 0.102158 0.636110 \n",
"932 -0.764774 0.065085 \n",
"657 0.942159 -0.957502 \n",
"\n",
"[800 rows x 9 columns], 'y': 905 -5.143310\n",
"702 23.877017\n",
"456 12.752919\n",
"271 6.385709\n",
"191 -5.492128\n",
" ... \n",
"862 1.585107\n",
"499 8.058945\n",
"373 12.268099\n",
"932 1.654133\n",
"657 12.117853\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 905 False\n",
"702 True\n",
"456 True\n",
"271 True\n",
"191 False\n",
" ... \n",
"862 True\n",
"499 True\n",
"373 True\n",
"932 True\n",
"657 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Control): 0.5509\n",
"INFO:causalml: sMAPE (Treatment): 0.1466\n",
"INFO:causalml: Gini (Control): 0.7399\n",
"INFO:causalml: Gini (Treatment): 0.9956\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9439\n",
"INFO:causalml: RMSE (Treatment): 0.7173\n",
"INFO:causalml: sMAPE (Control): 0.5251\n",
"INFO:causalml: sMAPE (Treatment): 0.1418\n",
"INFO:causalml: Gini (Control): 0.7563\n",
"INFO:causalml: Gini (Treatment): 0.9950\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"464 -1.458938 1.066085 -0.038658 -0.458790 0.563506 0.649619 0.007999 \n",
"142 -1.069820 -0.045581 1.088194 -0.352007 -0.407053 1.435721 1.821025 \n",
"216 -1.185130 1.723057 -2.436984 -0.947938 -0.797647 -1.237892 0.020300 \n",
"227 -0.757316 2.009235 -0.832385 1.297304 -0.017571 -1.484499 0.073045 \n",
"979 -1.320009 -0.547319 0.058828 1.048133 1.078373 -0.857583 0.480911 \n",
".. ... ... ... ... ... ... ... \n",
"172 -1.078399 1.722034 -1.675423 -0.026819 -0.577141 -1.633102 1.619526 \n",
"964 0.753382 0.592423 -0.618551 0.045706 -0.493502 -1.581298 -1.715736 \n",
"159 -1.948368 -0.614462 2.424841 -0.026382 -1.155858 0.788892 1.786834 \n",
"171 -1.142720 1.363980 -1.309646 -0.806699 -1.397706 0.591713 -0.245067 \n",
"499 -1.577195 -0.178122 0.207028 1.204303 -0.666172 1.145283 0.458618 \n",
"\n",
" X0 X1 \n",
"464 0.007999 0.649619 \n",
"142 1.821025 1.435721 \n",
"216 0.020300 -1.237892 \n",
"227 0.073045 -1.484499 \n",
"979 0.480911 -0.857583 \n",
".. ... ... \n",
"172 1.619526 -1.633102 \n",
"964 -1.715736 -1.581298 \n",
"159 1.786834 0.788892 \n",
"171 -0.245067 0.591713 \n",
"499 0.458618 1.145283 \n",
"\n",
"[800 rows x 9 columns], 'y': 464 8.601975\n",
"142 14.255984\n",
"216 2.376140\n",
"227 9.555874\n",
"979 10.393456\n",
" ... \n",
"172 10.160763\n",
"964 3.134159\n",
"159 9.334761\n",
"171 2.573635\n",
"499 8.058945\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 464 True\n",
"142 True\n",
"216 True\n",
"227 True\n",
"979 True\n",
" ... \n",
"172 True\n",
"964 True\n",
"159 True\n",
"171 True\n",
"499 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0551\n",
"INFO:causalml: RMSE (Treatment): 0.7052\n",
"INFO:causalml: sMAPE (Control): 0.5513\n",
"INFO:causalml: sMAPE (Treatment): 0.1489\n",
"INFO:causalml: Gini (Control): 0.7201\n",
"INFO:causalml: Gini (Treatment): 0.9948\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9918\n",
"INFO:causalml: RMSE (Treatment): 0.7138\n",
"INFO:causalml: sMAPE (Control): 0.5188\n",
"INFO:causalml: sMAPE (Treatment): 0.1520\n",
"INFO:causalml: Gini (Control): 0.7273\n",
"INFO:causalml: Gini (Treatment): 0.9944\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9832\n",
"INFO:causalml: RMSE (Treatment): 0.7111\n",
"INFO:causalml: sMAPE (Control): 0.5083\n",
"INFO:causalml: sMAPE (Treatment): 0.1456\n",
"INFO:causalml: Gini (Control): 0.7489\n",
"INFO:causalml: Gini (Treatment): 0.9949\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0359\n",
"INFO:causalml: RMSE (Treatment): 0.6357\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"616 -1.830382 0.390144 1.344299 0.522921 -0.493492 -0.066279 -0.126978 \n",
"441 0.096609 1.592914 -1.201277 -1.185385 -0.135004 0.210872 2.310466 \n",
"743 -1.698782 0.965766 1.076097 -1.380195 -0.166558 -1.229501 -1.256471 \n",
"565 0.489147 1.219821 -1.070019 0.305099 0.624808 -0.830622 1.045480 \n",
"958 -0.495343 0.847053 -0.028871 -0.718100 -0.871586 -0.630299 -0.132512 \n",
".. ... ... ... ... ... ... ... \n",
"364 -1.078590 1.563574 1.504080 -0.486241 -1.540033 -0.435152 0.956055 \n",
"965 -0.258222 2.196243 -1.509052 0.242610 -0.852991 -0.125788 3.020499 \n",
"755 -0.785170 -0.634127 0.249551 -0.613209 0.804813 -1.015528 1.452350 \n",
"7 0.174130 1.506210 -1.549595 0.782615 -1.878625 -0.626575 1.176085 \n",
"137 -1.894324 0.875532 -1.274949 -0.005126 -1.668351 -0.299636 -1.162263 \n",
"\n",
" X0 X1 \n",
"616 -0.126978 -0.066279 \n",
"441 2.310466 0.210872 \n",
"743 -1.256471 -1.229501 \n",
"565 1.045480 -0.830622 \n",
"958 -0.132512 -0.630299 \n",
".. ... ... \n",
"364 0.956055 -0.435152 \n",
"965 3.020499 -0.125788 \n",
"755 1.452350 -1.015528 \n",
"7 1.176085 -0.626575 \n",
"137 -1.162263 -0.299636 \n",
"\n",
"[800 rows x 9 columns], 'y': 616 4.604313\n",
"441 18.248395\n",
"743 -1.497744\n",
"565 17.014565\n",
"958 5.051010\n",
" ... \n",
"364 7.693260\n",
"965 19.735711\n",
"755 12.541520\n",
"7 -2.646268\n",
"137 -9.707122\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 616 True\n",
"441 True\n",
"743 True\n",
"565 True\n",
"958 True\n",
" ... \n",
"364 True\n",
"965 True\n",
"755 True\n",
"7 False\n",
"137 False\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"671 -1.805567 1.966280 0.355705 0.675031 -1.531579 -0.217720 -0.701737 \n",
"16 -0.022696 0.467212 0.670827 -0.516231 -0.682599 1.362676 1.401631 \n",
"353 -0.079900 1.001506 -0.228440 -0.144542 -1.332903 0.275099 2.373142 \n",
"480 -0.377755 0.312070 0.399832 1.532559 0.348411 0.225440 0.753623 \n",
"452 -1.226268 1.393577 0.501651 1.386900 -0.728663 -2.554742 0.738239 \n",
".. ... ... ... ... ... ... ... \n",
"404 0.269959 -1.426995 -0.624877 1.207414 -0.632554 0.826927 -0.342909 \n",
"339 -0.174635 1.102320 -0.538718 1.353400 -2.411045 -0.088519 -0.223676 \n",
"538 -1.000985 2.417859 0.027563 1.436945 1.891137 -2.054885 0.763508 \n",
"959 -1.282991 1.134722 -1.881201 0.830477 0.965414 1.414359 -1.140110 \n",
"49 -0.982952 2.225363 -1.526931 -0.694241 0.029243 -0.455032 0.234971 \n",
"\n",
" X0 X1 \n",
"671 -0.701737 -0.217720 \n",
"16 1.401631 1.362676 \n",
"353 2.373142 0.275099 \n",
"480 0.753623 0.225440 \n",
"452 0.738239 -2.554742 \n",
".. ... ... \n",
"404 -0.342909 0.826927 \n",
"339 -0.223676 -0.088519 \n",
"538 0.763508 -2.054885 \n",
"959 -1.140110 1.414359 \n",
"49 0.234971 -0.455032 \n",
"\n",
"[800 rows x 9 columns], 'y': 671 -6.150284\n",
"16 15.151640\n",
"353 16.031327\n",
"480 15.267030\n",
"452 7.577603\n",
" ... \n",
"404 -1.323791\n",
"339 4.815990\n",
"538 16.761939\n",
"959 7.562645\n",
"49 8.274390\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 671 False\n",
"16 True\n",
"353 True\n",
"480 True\n",
"452 True\n",
" ... \n",
"404 False\n",
"339 True\n",
"538 True\n",
"959 True\n",
"49 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"818 -2.294772 1.081263 -0.110948 0.240523 -2.452061 -0.676310 1.024324 \n",
"441 0.096609 1.592914 -1.201277 -1.185385 -0.135004 0.210872 2.310466 \n",
"660 -1.323851 1.441421 0.356298 -1.619747 1.211768 -0.134022 0.702311 \n",
"907 -0.596246 0.342098 0.422666 1.205834 -0.189522 -0.157053 1.625369 \n",
"150 -2.720297 0.875790 -0.364609 -0.201468 0.001957 -1.103898 0.006235 \n",
".. ... ... ... ... ... ... ... \n",
"569 -2.828271 0.496613 0.605941 1.991670 1.438382 -1.427814 2.464037 \n",
"174 -1.209450 2.573548 -1.875041 0.466606 -2.593907 -0.787080 0.157327 \n",
"618 -0.852827 2.817516 0.994045 0.632066 1.475903 -1.788458 -0.354627 \n",
"578 -0.125015 -0.484719 -0.554158 -0.644919 -0.352951 -0.169403 0.807828 \n",
"851 -1.580498 2.916707 -1.305058 0.877563 0.733961 -1.876975 2.623342 \n",
"\n",
" X0 X1 \n",
"818 1.024324 -0.676310 \n",
"441 2.310466 0.210872 \n",
"660 0.702311 -0.134022 \n",
"907 1.625369 -0.157053 \n",
"150 0.006235 -1.103898 \n",
".. ... ... \n",
"569 2.464037 -1.427814 \n",
"174 0.157327 -0.787080 \n",
"618 -0.354627 -1.788458 \n",
"578 0.807828 -0.169403 \n",
"851 2.623342 -1.876975 \n",
"\n",
"[800 rows x 9 columns], 'y': 818 -11.770683\n",
"441 18.248395\n",
"660 11.491007\n",
"907 15.569400\n",
"150 1.212438\n",
" ... \n",
"569 16.043709\n",
"174 1.619585\n",
"618 12.340446\n",
"578 -2.946371\n",
"851 18.155148\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 818 False\n",
"441 True\n",
"660 True\n",
"907 True\n",
"150 True\n",
" ... \n",
"569 True\n",
"174 True\n",
"618 True\n",
"578 False\n",
"851 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"14 -2.078772 1.196831 1.691383 -0.112157 -0.733879 -1.146159 0.723966 \n",
"287 -2.059567 -0.680278 -2.183921 1.320290 -1.748679 0.879174 0.780425 \n",
"429 -0.310856 1.184695 0.500596 1.413069 -1.423834 -2.391290 -0.004592 \n",
"806 -0.731126 0.113323 0.240405 0.593963 -1.053335 -1.951187 2.046437 \n",
"89 -0.956579 0.802858 -0.416669 0.542902 -0.031293 1.430714 1.345825 \n",
".. ... ... ... ... ... ... ... \n",
"546 -2.888766 0.186649 0.030494 0.288355 -1.192114 -0.335307 1.980589 \n",
"279 -2.042622 -0.483061 -0.027763 0.310754 0.006903 0.717223 1.415660 \n",
"732 -1.876595 1.781727 -0.587176 0.704493 -0.050272 -0.901322 2.047330 \n",
"642 -1.263921 0.248475 -1.146400 0.651563 -0.523927 0.513736 -0.429372 \n",
"614 -2.592181 1.103790 -0.570001 0.139735 0.205595 -0.448537 -0.211413 \n",
"\n",
" X0 X1 \n",
"14 0.723966 -1.146159 \n",
"287 0.780425 0.879174 \n",
"429 -0.004592 -2.391290 \n",
"806 2.046437 -1.951187 \n",
"89 1.345825 1.430714 \n",
".. ... ... \n",
"546 1.980589 -0.335307 \n",
"279 1.415660 0.717223 \n",
"732 2.047330 -0.901322 \n",
"642 -0.429372 0.513736 \n",
"614 -0.211413 -0.448537 \n",
"\n",
"[800 rows x 9 columns], 'y': 14 5.340331\n",
"287 -11.017227\n",
"429 5.762485\n",
"806 11.154196\n",
"89 15.058027\n",
" ... \n",
"546 -11.053337\n",
"279 -6.155145\n",
"732 -3.133831\n",
"642 4.413860\n",
"614 -5.992837\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 14 True\n",
"287 False\n",
"429 True\n",
"806 True\n",
"89 True\n",
" ... \n",
"546 False\n",
"279 False\n",
"732 False\n",
"642 True\n",
"614 False\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Control): 0.5428\n",
"INFO:causalml: sMAPE (Treatment): 0.1382\n",
"INFO:causalml: Gini (Control): 0.7706\n",
"INFO:causalml: Gini (Treatment): 0.9965\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9307\n",
"INFO:causalml: RMSE (Treatment): 0.6748\n",
"INFO:causalml: sMAPE (Control): 0.5415\n",
"INFO:causalml: sMAPE (Treatment): 0.1462\n",
"INFO:causalml: Gini (Control): 0.7507\n",
"INFO:causalml: Gini (Treatment): 0.9955\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0459\n",
"INFO:causalml: RMSE (Treatment): 0.6579\n",
"INFO:causalml: sMAPE (Control): 0.5067\n",
"INFO:causalml: sMAPE (Treatment): 0.1369\n",
"INFO:causalml: Gini (Control): 0.7317\n",
"INFO:causalml: Gini (Treatment): 0.9958\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"445 -0.093150 0.159054 1.219054 2.768834 -0.538133 -0.508483 1.145099 \n",
"1 -0.612423 0.591947 -1.160895 1.779737 -1.083905 0.802709 0.713931 \n",
"259 0.990156 0.812084 -0.696832 -1.386308 -1.233495 1.282252 1.415275 \n",
"150 -2.720297 0.875790 -0.364609 -0.201468 0.001957 -1.103898 0.006235 \n",
"845 -1.215958 -0.485289 -1.167214 0.914403 -1.773236 0.103620 -0.518889 \n",
".. ... ... ... ... ... ... ... \n",
"400 -0.340443 -1.712856 0.329522 1.261908 -0.358655 0.905733 1.694371 \n",
"340 0.375616 2.217136 -0.666164 -0.970808 0.446217 -0.864206 1.763119 \n",
"187 0.166212 1.756207 0.174379 0.739862 -0.309621 0.009743 -0.960418 \n",
"53 -2.930083 0.879228 -0.579348 0.759465 -0.462352 -2.221874 2.337682 \n",
"392 0.391364 -0.989100 -0.665144 -0.463719 1.025926 0.692766 -0.147856 \n",
"\n",
" X0 X1 \n",
"445 1.145099 -0.508483 \n",
"1 0.713931 0.802709 \n",
"259 1.415275 1.282252 \n",
"150 0.006235 -1.103898 \n",
"845 -0.518889 0.103620 \n",
".. ... ... \n",
"400 1.694371 0.905733 \n",
"340 1.763119 -0.864206 \n",
"187 -0.960418 0.009743 \n",
"53 2.337682 -2.221874 \n",
"392 -0.147856 0.692766 \n",
"\n",
"[800 rows x 9 columns], 'y': 445 16.120442\n",
"1 11.125450\n",
"259 15.171532\n",
"150 1.212438\n",
"845 -8.309502\n",
" ... \n",
"400 15.161858\n",
"340 18.529106\n",
"187 9.138120\n",
"53 7.558222\n",
"392 12.111471\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 445 True\n",
"1 True\n",
"259 True\n",
"150 True\n",
"845 False\n",
" ... \n",
"400 True\n",
"340 True\n",
"187 True\n",
"53 True\n",
"392 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"877 -0.359565 0.826991 -0.179359 -1.811258 1.418593 1.405163 -0.075609 \n",
"948 0.137207 0.807432 0.012233 -0.321381 -1.727172 -0.265376 1.037937 \n",
"906 -1.727409 0.768134 0.127817 2.012317 -0.001440 0.690172 0.662514 \n",
"531 -1.304303 -0.065667 -1.069981 0.533144 1.279208 -0.691485 -0.259040 \n",
"447 -0.405581 0.228388 -0.346772 -0.788431 -0.808578 -1.461212 -0.094927 \n",
".. ... ... ... ... ... ... ... \n",
"251 -2.083898 -0.553302 0.342455 1.195262 -0.140618 -0.475982 0.394482 \n",
"329 -0.721478 0.206043 0.019891 0.334449 -2.015860 0.081232 2.050940 \n",
"742 -1.894608 0.965814 -2.625684 -0.050702 0.089967 -0.260953 2.020572 \n",
"291 0.257877 -0.247493 -0.219583 0.077716 -0.585046 0.209063 2.522633 \n",
"984 -0.468952 1.419946 -1.223588 -0.200703 0.818225 -1.741452 0.553023 \n",
"\n",
" X0 X1 \n",
"877 -0.075609 1.405163 \n",
"948 1.037937 -0.265376 \n",
"906 0.662514 0.690172 \n",
"531 -0.259040 -0.691485 \n",
"447 -0.094927 -1.461212 \n",
".. ... ... \n",
"251 0.394482 -0.475982 \n",
"329 2.050940 0.081232 \n",
"742 2.020572 -0.260953 \n",
"291 2.522633 0.209063 \n",
"984 0.553023 -1.741452 \n",
"\n",
"[800 rows x 9 columns], 'y': 877 12.698322\n",
"948 9.814477\n",
"906 11.554534\n",
"531 7.735845\n",
"447 3.661764\n",
" ... \n",
"251 5.436045\n",
"329 10.777317\n",
"742 11.254116\n",
"291 18.348778\n",
"984 11.332827\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 877 True\n",
"948 True\n",
"906 True\n",
"531 True\n",
"447 True\n",
" ... \n",
"251 True\n",
"329 True\n",
"742 True\n",
"291 True\n",
"984 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0274\n",
"INFO:causalml: RMSE (Treatment): 0.6404\n",
"INFO:causalml: sMAPE (Control): 0.5697\n",
"INFO:causalml: sMAPE (Treatment): 0.1380\n",
"INFO:causalml: Gini (Control): 0.7555\n",
"INFO:causalml: Gini (Treatment): 0.9963\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1547\n",
"INFO:causalml: RMSE (Treatment): 0.7120\n",
"INFO:causalml: sMAPE (Control): 0.5614\n",
"INFO:causalml: sMAPE (Treatment): 0.1525\n",
"INFO:causalml: Gini (Control): 0.7153\n",
"INFO:causalml: Gini (Treatment): 0.9952\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"333 -0.903943 1.303046 -0.373566 0.414038 0.837871 -0.085945 0.009761 \n",
"600 -2.132836 0.973990 -0.800368 0.209837 -1.462109 -0.379117 -0.646683 \n",
"296 -0.693534 1.091959 0.118506 1.534617 -1.801390 0.094255 1.938525 \n",
"769 -1.913252 0.670484 -0.368978 -0.244239 -0.164518 0.285367 1.858510 \n",
"877 -0.359565 0.826991 -0.179359 -1.811258 1.418593 1.405163 -0.075609 \n",
".. ... ... ... ... ... ... ... \n",
"463 -1.384868 1.213614 -1.227984 0.542554 0.851906 1.016157 0.823336 \n",
"129 -0.761571 -0.442922 -1.576714 1.024327 -0.544396 -1.884830 0.425136 \n",
"309 -0.539594 2.000693 1.298256 0.596046 -2.741050 -0.539698 0.178355 \n",
"734 -2.534543 1.297053 -0.227526 -0.605377 -1.703765 -0.280342 0.415044 \n",
"657 -1.103857 0.975798 0.409175 0.707782 0.346495 -0.957502 0.942159 \n",
"\n",
" X0 X1 \n",
"333 0.009761 -0.085945 \n",
"600 -0.646683 -0.379117 \n",
"296 1.938525 0.094255 \n",
"769 1.858510 0.285367 \n",
"877 -0.075609 1.405163 \n",
".. ... ... \n",
"463 0.823336 1.016157 \n",
"129 0.425136 -1.884830 \n",
"309 0.178355 -0.539698 \n",
"734 0.415044 -0.280342 \n",
"657 0.942159 -0.957502 \n",
"\n",
"[800 rows x 9 columns], 'y': 333 11.223406\n",
"600 -2.038553\n",
"296 13.572697\n",
"769 -5.821681\n",
"877 12.698322\n",
" ... \n",
"463 13.719677\n",
"129 5.487315\n",
"309 4.869483\n",
"734 -0.213830\n",
"657 12.117853\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 333 True\n",
"600 True\n",
"296 True\n",
"769 False\n",
"877 True\n",
" ... \n",
"463 True\n",
"129 True\n",
"309 True\n",
"734 True\n",
"657 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"866 -2.492060 0.007731 -0.940262 -0.496057 1.104068 0.043952 0.520345 \n",
"426 -0.479176 1.237188 -0.726685 -0.314696 0.767152 -0.740509 -0.373064 \n",
"930 0.236247 0.615868 -0.483059 -0.462038 0.084682 1.027238 -0.515047 \n",
"649 -2.154564 1.280369 -0.834181 1.662134 -0.490512 0.051132 1.668837 \n",
"41 0.075080 -0.116355 -0.810933 2.222729 -0.150383 0.082462 -0.018712 \n",
".. ... ... ... ... ... ... ... \n",
"401 -1.899841 1.517762 -1.564655 1.267017 -0.269945 0.348545 0.354959 \n",
"90 0.097345 0.533868 -1.112249 0.999274 0.230117 -1.125698 0.717416 \n",
"290 0.299643 -1.028151 0.230684 -0.517070 0.924262 -1.450635 0.759954 \n",
"464 -1.458938 1.066085 -0.038658 -0.458790 0.563506 0.649619 0.007999 \n",
"81 -2.075409 -0.271621 -0.306578 1.035155 0.414936 -0.005484 0.350309 \n",
"\n",
" X0 X1 \n",
"866 0.520345 0.043952 \n",
"426 -0.373064 -0.740509 \n",
"930 -0.515047 1.027238 \n",
"649 1.668837 0.051132 \n",
"41 -0.018712 0.082462 \n",
".. ... ... \n",
"401 0.354959 0.348545 \n",
"90 0.717416 -1.125698 \n",
"290 0.759954 -1.450635 \n",
"464 0.007999 0.649619 \n",
"81 0.350309 -0.005484 \n",
"\n",
"[800 rows x 9 columns], 'y': 866 6.414822\n",
"426 8.947595\n",
"930 10.114187\n",
"649 11.515556\n",
"41 12.011428\n",
" ... \n",
"401 7.798611\n",
"90 13.341932\n",
"290 12.664366\n",
"464 8.601975\n",
"81 7.063312\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 866 True\n",
"426 True\n",
"930 True\n",
"649 True\n",
"41 True\n",
" ... \n",
"401 True\n",
"90 True\n",
"290 True\n",
"464 True\n",
"81 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0349\n",
"INFO:causalml: RMSE (Treatment): 0.7215\n",
"INFO:causalml: sMAPE (Control): 0.5344\n",
"INFO:causalml: sMAPE (Treatment): 0.1396\n",
"INFO:causalml: Gini (Control): 0.7418\n",
"INFO:causalml: Gini (Treatment): 0.9949\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0516\n",
"INFO:causalml: RMSE (Treatment): 0.7011\n",
"INFO:causalml: sMAPE (Control): 0.5438\n",
"INFO:causalml: sMAPE (Treatment): 0.1449\n",
"INFO:causalml: Gini (Control): 0.6956\n",
"INFO:causalml: Gini (Treatment): 0.9943\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"311 -0.455229 -0.695322 0.209254 -0.763416 -1.934863 -0.037463 1.273690 \n",
"120 0.925627 1.150181 -0.372738 1.184429 -1.147969 -0.032255 0.385113 \n",
"429 -0.310856 1.184695 0.500596 1.413069 -1.423834 -2.391290 -0.004592 \n",
"322 0.441476 -0.310950 -0.945903 -1.463723 -2.023630 -0.785600 1.276769 \n",
"86 -1.171030 0.999708 -0.474575 -0.002353 1.214732 -0.432952 -1.419492 \n",
".. ... ... ... ... ... ... ... \n",
"848 -1.892578 1.453947 -2.287147 0.205164 -0.295408 -1.312249 0.506585 \n",
"980 -1.323765 1.378697 -1.383845 -0.811301 -1.301810 0.635665 1.740498 \n",
"569 -2.828271 0.496613 0.605941 1.991670 1.438382 -1.427814 2.464037 \n",
"127 -1.184442 1.633850 -0.489806 0.806896 -0.654125 -0.324203 -1.297638 \n",
"502 -1.817353 2.122041 -2.913312 1.536276 -0.699921 -0.900995 1.371631 \n",
"\n",
" X0 X1 \n",
"311 1.273690 -0.037463 \n",
"120 0.385113 -0.032255 \n",
"429 -0.004592 -2.391290 \n",
"322 1.276769 -0.785600 \n",
"86 -1.419492 -0.432952 \n",
".. ... ... \n",
"848 0.506585 -1.312249 \n",
"980 1.740498 0.635665 \n",
"569 2.464037 -1.427814 \n",
"127 -1.297638 -0.324203 \n",
"502 1.371631 -0.900995 \n",
"\n",
"[800 rows x 9 columns], 'y': 311 6.548500\n",
"120 13.587443\n",
"429 5.762485\n",
"322 7.004125\n",
"86 4.893758\n",
" ... \n",
"848 -5.747920\n",
"980 9.526360\n",
"569 16.043709\n",
"127 2.218793\n",
"502 9.319528\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 311 True\n",
"120 True\n",
"429 True\n",
"322 True\n",
"86 True\n",
" ... \n",
"848 False\n",
"980 True\n",
"569 True\n",
"127 True\n",
"502 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"572 -2.548463 -0.098732 -0.957317 0.639523 -0.249403 1.104659 -0.047994 \n",
"221 -1.138293 0.865923 -1.177382 -0.509397 -2.476264 1.589415 0.625094 \n",
"352 -0.314322 -0.439802 2.366604 0.701073 -0.609823 0.603549 -0.256803 \n",
"850 -1.559250 1.506724 0.449602 0.956745 -1.983230 -0.410145 2.749812 \n",
"582 -1.856578 1.519025 -0.354817 1.777607 -0.103456 -0.540906 0.894554 \n",
".. ... ... ... ... ... ... ... \n",
"980 -1.323765 1.378697 -1.383845 -0.811301 -1.301810 0.635665 1.740498 \n",
"811 -1.803447 0.510549 -1.668762 1.850597 -0.358186 -2.226574 2.080508 \n",
"789 -1.419550 -0.199775 1.439845 -0.043468 -0.070078 0.347234 1.237806 \n",
"883 -1.053935 -0.020818 -0.218070 -0.163484 -1.436398 0.888213 0.560956 \n",
"164 -1.968714 0.633982 -0.864566 0.029554 -0.968736 -1.092772 0.316363 \n",
"\n",
" X0 X1 \n",
"572 -0.047994 1.104659 \n",
"221 0.625094 1.589415 \n",
"352 -0.256803 0.603549 \n",
"850 2.749812 -0.410145 \n",
"582 0.894554 -0.540906 \n",
".. ... ... \n",
"980 1.740498 0.635665 \n",
"811 2.080508 -2.226574 \n",
"789 1.237806 0.347234 \n",
"883 0.560956 0.888213 \n",
"164 0.316363 -1.092772 \n",
"\n",
"[800 rows x 9 columns], 'y': 572 -8.024569\n",
"221 -10.175373\n",
"352 8.970110\n",
"850 12.795708\n",
"582 10.549091\n",
" ... \n",
"980 9.526360\n",
"811 10.541175\n",
"789 11.103887\n",
"883 5.901248\n",
"164 1.757613\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 572 False\n",
"221 False\n",
"352 True\n",
"850 True\n",
"582 True\n",
" ... \n",
"980 True\n",
"811 True\n",
"789 True\n",
"883 True\n",
"164 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0202\n",
"INFO:causalml: RMSE (Treatment): 0.6791\n",
"INFO:causalml: sMAPE (Control): 0.5553\n",
"INFO:causalml: sMAPE (Treatment): 0.1368\n",
"INFO:causalml: Gini (Control): 0.7275\n",
"INFO:causalml: Gini (Treatment): 0.9955\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0764\n",
"INFO:causalml: RMSE (Treatment): 0.7291\n",
"INFO:causalml: sMAPE (Control): 0.5471\n",
"INFO:causalml: sMAPE (Treatment): 0.1549\n",
"INFO:causalml: Gini (Control): 0.7327\n",
"INFO:causalml: Gini (Treatment): 0.9950\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"720 -0.178107 0.545358 -1.188979 -2.009287 -1.712708 0.183073 2.293672 \n",
"794 -1.233396 -0.435764 -0.476026 0.431914 0.233094 -1.611720 0.182139 \n",
"964 0.753382 0.592423 -0.618551 0.045706 -0.493502 -1.581298 -1.715736 \n",
"738 -0.532602 1.680468 -1.570149 2.071328 -0.883423 -2.507779 0.510461 \n",
"813 -2.510734 1.799308 -1.388065 0.395407 0.345451 -1.133800 0.417955 \n",
".. ... ... ... ... ... ... ... \n",
"386 0.144990 2.200438 2.535124 -0.009489 -1.157723 1.477828 -0.341690 \n",
"975 -0.891387 0.694594 0.240509 1.789194 -0.619145 -1.193071 0.675476 \n",
"731 -0.792085 2.126007 -1.140046 0.187479 -0.140220 -0.338651 1.396477 \n",
"807 0.395378 2.501552 0.964778 0.986126 -0.742713 -1.556838 -0.017120 \n",
"419 -0.220525 -0.582744 0.201690 0.269966 -0.821470 1.089105 1.028671 \n",
"\n",
" X0 X1 \n",
"720 2.293672 0.183073 \n",
"794 0.182139 -1.611720 \n",
"964 -1.715736 -1.581298 \n",
"738 0.510461 -2.507779 \n",
"813 0.417955 -1.133800 \n",
".. ... ... \n",
"386 -0.341690 1.477828 \n",
"975 0.675476 -1.193071 \n",
"731 1.396477 -0.338651 \n",
"807 -0.017120 -1.556838 \n",
"419 1.028671 1.089105 \n",
"\n",
"[800 rows x 9 columns], 'y': 720 11.018665\n",
"794 5.470791\n",
"964 3.134159\n",
"738 8.470271\n",
"813 5.390940\n",
" ... \n",
"386 11.776341\n",
"975 9.860888\n",
"731 13.912472\n",
"807 11.784838\n",
"419 12.060932\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 720 True\n",
"794 True\n",
"964 True\n",
"738 True\n",
"813 True\n",
" ... \n",
"386 True\n",
"975 True\n",
"731 True\n",
"807 True\n",
"419 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"395 -1.795386 0.484380 -0.308798 0.356361 0.933391 -1.198681 0.592933 \n",
"991 -0.283700 0.699498 0.106398 -1.059915 -2.411506 -0.204126 1.891908 \n",
"336 -3.093855 1.469971 0.367279 1.174817 -0.729766 -2.114001 0.885400 \n",
"344 -2.504980 1.383036 -0.141410 -0.360349 -1.132968 -0.013613 1.887884 \n",
"773 -1.096836 -0.432951 -0.837524 -0.231672 -0.535628 -1.020083 -0.353180 \n",
".. ... ... ... ... ... ... ... \n",
"979 -1.320009 -0.547319 0.058828 1.048133 1.078373 -0.857583 0.480911 \n",
"131 -0.636926 1.412123 -1.568989 1.128580 -1.107298 -0.356463 3.129605 \n",
"409 -0.542454 0.428394 1.357376 0.037163 -0.476170 1.308313 2.058995 \n",
"807 0.395378 2.501552 0.964778 0.986126 -0.742713 -1.556838 -0.017120 \n",
"594 -0.485088 0.198923 0.215890 1.387663 0.491391 -1.501354 -0.527038 \n",
"\n",
" X0 X1 \n",
"395 0.592933 -1.198681 \n",
"991 1.891908 -0.204126 \n",
"336 0.885400 -2.114001 \n",
"344 1.887884 -0.013613 \n",
"773 -0.353180 -1.020083 \n",
".. ... ... \n",
"979 0.480911 -0.857583 \n",
"131 3.129605 -0.356463 \n",
"409 2.058995 1.308313 \n",
"807 -0.017120 -1.556838 \n",
"594 -0.527038 -1.501354 \n",
"\n",
"[800 rows x 9 columns], 'y': 395 8.681410\n",
"991 -7.648859\n",
"336 2.926169\n",
"344 -9.322258\n",
"773 1.630931\n",
" ... \n",
"979 10.393456\n",
"131 18.255680\n",
"409 17.513846\n",
"807 11.784838\n",
"594 8.167200\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 395 True\n",
"991 False\n",
"336 True\n",
"344 False\n",
"773 True\n",
" ... \n",
"979 True\n",
"131 True\n",
"409 True\n",
"807 True\n",
"594 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0838\n",
"INFO:causalml: RMSE (Treatment): 0.7578\n",
"INFO:causalml: sMAPE (Control): 0.5357\n",
"INFO:causalml: sMAPE (Treatment): 0.1493\n",
"INFO:causalml: Gini (Control): 0.7243\n",
"INFO:causalml: Gini (Treatment): 0.9939\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0498\n",
"INFO:causalml: RMSE (Treatment): 0.7149\n",
"INFO:causalml: sMAPE (Control): 0.5699\n",
"INFO:causalml: sMAPE (Treatment): 0.1427\n",
"INFO:causalml: Gini (Control): 0.7412\n",
"INFO:causalml: Gini (Treatment): 0.9951\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0743\n",
"INFO:causalml: RMSE (Treatment): 0.7071\n",
"INFO:causalml: sMAPE (Control): 0.5430\n",
"INFO:causalml: sMAPE (Treatment): 0.1417\n",
"INFO:causalml: Gini (Control): 0.7392\n",
"INFO:causalml: Gini (Treatment): 0.9952\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"893 -1.491824 0.452183 -1.205184 0.340149 1.739224 -0.632809 0.210046 \n",
"456 -1.180741 1.678642 0.789419 1.087730 0.037308 0.563234 0.500563 \n",
"368 -2.298170 1.146523 -1.218792 -0.454870 -0.475421 -1.530454 0.327366 \n",
"558 -0.550544 0.440434 -0.750181 1.612420 0.551244 0.168593 1.698572 \n",
"287 -2.059567 -0.680278 -2.183921 1.320290 -1.748679 0.879174 0.780425 \n",
".. ... ... ... ... ... ... ... \n",
"935 -1.612218 0.425603 0.450798 1.795192 -1.030650 0.071948 0.533019 \n",
"148 -0.302752 -0.391139 -1.411531 -0.984236 -1.414685 -1.430286 1.022603 \n",
"628 -0.907745 0.221924 -0.912103 0.680653 -0.919082 0.488610 -0.844236 \n",
"356 -1.087973 1.063452 -0.582690 -0.391019 -0.717769 0.567377 0.968256 \n",
"39 -0.961481 1.275170 -1.485334 -0.763943 -1.116911 0.131391 1.712740 \n",
"\n",
" X0 X1 \n",
"893 0.210046 -0.632809 \n",
"456 0.500563 0.563234 \n",
"368 0.327366 -1.530454 \n",
"558 1.698572 0.168593 \n",
"287 0.780425 0.879174 \n",
".. ... ... \n",
"935 0.533019 0.071948 \n",
"148 1.022603 -1.430286 \n",
"628 -0.844236 0.488610 \n",
"356 0.968256 0.567377 \n",
"39 1.712740 0.131391 \n",
"\n",
"[800 rows x 9 columns], 'y': 893 10.444157\n",
"456 12.752919\n",
"368 1.367327\n",
"558 18.249687\n",
"287 -11.017227\n",
" ... \n",
"935 7.518694\n",
"148 4.946877\n",
"628 -4.505402\n",
"356 -4.713297\n",
"39 -6.109185\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 893 True\n",
"456 True\n",
"368 True\n",
"558 True\n",
"287 False\n",
" ... \n",
"935 True\n",
"148 True\n",
"628 False\n",
"356 False\n",
"39 False\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"900 -1.528877 0.854363 -0.020892 -0.125090 1.283825 0.408115 -0.168281 \n",
"363 -1.299538 1.492361 -0.176621 -0.788150 -0.271734 -2.617038 1.663896 \n",
"692 -1.425142 -0.090768 -2.394217 0.281658 -1.069567 -1.236345 -1.514344 \n",
"226 -1.070717 0.609382 -3.049646 1.477891 -0.338049 0.433746 0.098532 \n",
"856 -2.193329 0.959709 -2.021881 -0.809934 -2.201305 -2.098867 -0.278113 \n",
".. ... ... ... ... ... ... ... \n",
"98 -0.724707 -0.167508 -2.200046 1.316365 -0.652669 1.877820 0.372580 \n",
"531 -1.304303 -0.065667 -1.069981 0.533144 1.279208 -0.691485 -0.259040 \n",
"607 -0.713550 -0.806284 -0.339457 -0.279553 -0.477920 -0.690686 -0.221325 \n",
"522 0.488747 0.260763 -0.084712 2.012467 0.136177 -0.514404 1.454348 \n",
"813 -2.510734 1.799308 -1.388065 0.395407 0.345451 -1.133800 0.417955 \n",
"\n",
" X0 X1 \n",
"900 -0.168281 0.408115 \n",
"363 1.663896 -2.617038 \n",
"692 -1.514344 -1.236345 \n",
"226 0.098532 0.433746 \n",
"856 -0.278113 -2.098867 \n",
".. ... ... \n",
"98 0.372580 1.877820 \n",
"531 -0.259040 -0.691485 \n",
"607 -0.221325 -0.690686 \n",
"522 1.454348 -0.514404 \n",
"813 0.417955 -1.133800 \n",
"\n",
"[800 rows x 9 columns], 'y': 900 9.560536\n",
"363 8.937908\n",
"692 -8.079772\n",
"226 -3.230951\n",
"856 -13.300039\n",
" ... \n",
"98 -3.658929\n",
"531 7.735845\n",
"607 3.571733\n",
"522 19.196033\n",
"813 5.390940\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 900 True\n",
"363 True\n",
"692 False\n",
"226 False\n",
"856 False\n",
" ... \n",
"98 False\n",
"531 True\n",
"607 True\n",
"522 True\n",
"813 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"695 -0.197821 -0.512162 0.067361 0.935517 -0.381064 -1.154606 0.317349 \n",
"486 -0.131758 -0.288235 -0.600001 0.396915 0.962738 -1.026124 0.537855 \n",
"43 -0.954425 1.158370 0.086873 0.568439 -0.884157 -0.410623 1.426287 \n",
"418 -1.575082 -0.671851 -0.031660 -0.315301 0.096593 0.101204 -0.833223 \n",
"122 -2.951921 -0.276981 -1.228503 0.841593 0.132569 -1.710101 0.432787 \n",
".. ... ... ... ... ... ... ... \n",
"142 -1.069820 -0.045581 1.088194 -0.352007 -0.407053 1.435721 1.821025 \n",
"667 -2.198198 0.869380 0.049001 1.338380 -0.046996 0.313350 1.524353 \n",
"560 -0.591932 0.047587 -1.357618 -1.008455 -1.341175 0.311592 1.261070 \n",
"355 -0.326494 2.433717 0.080853 1.915810 -0.164674 0.466589 -0.152236 \n",
"738 -0.532602 1.680468 -1.570149 2.071328 -0.883423 -2.507779 0.510461 \n",
"\n",
" X0 X1 \n",
"695 0.317349 -1.154606 \n",
"486 0.537855 -1.026124 \n",
"43 1.426287 -0.410623 \n",
"418 -0.833223 0.101204 \n",
"122 0.432787 -1.710101 \n",
".. ... ... \n",
"142 1.821025 1.435721 \n",
"667 1.524353 0.313350 \n",
"560 1.261070 0.311592 \n",
"355 -0.152236 0.466589 \n",
"738 0.510461 -2.507779 \n",
"\n",
"[800 rows x 9 columns], 'y': 695 8.766754\n",
"486 12.711964\n",
"43 11.590880\n",
"418 -5.534462\n",
"122 -8.281570\n",
" ... \n",
"142 14.255984\n",
"667 11.985876\n",
"560 -7.215301\n",
"355 13.712619\n",
"738 8.470271\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 695 True\n",
"486 True\n",
"43 True\n",
"418 False\n",
"122 False\n",
" ... \n",
"142 True\n",
"667 True\n",
"560 False\n",
"355 True\n",
"738 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0525\n",
"INFO:causalml: RMSE (Treatment): 0.7187\n",
"INFO:causalml: sMAPE (Control): 0.5312\n",
"INFO:causalml: sMAPE (Treatment): 0.1296\n",
"INFO:causalml: Gini (Control): 0.7540\n",
"INFO:causalml: Gini (Treatment): 0.9948\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1200\n",
"INFO:causalml: RMSE (Treatment): 0.7349\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"640 -1.678246 1.438381 -0.155114 1.449979 -1.577508 -0.659106 0.118324 \n",
"827 -0.123357 -0.188634 0.382711 -0.500811 1.739447 0.613970 0.748232 \n",
"673 -0.941288 -0.348886 -1.817197 -0.426040 -0.139103 -0.649783 0.857110 \n",
"284 -0.929719 0.896653 -0.016749 -0.051014 -2.466214 -0.577574 0.515098 \n",
"295 -0.983868 1.058323 -1.353941 0.550563 -1.213079 0.165130 1.300355 \n",
".. ... ... ... ... ... ... ... \n",
"178 -1.287866 0.105602 0.132745 -0.181920 -0.151254 -1.038557 0.408551 \n",
"723 -0.107996 1.393754 0.825508 0.735281 -0.586520 -0.348836 0.538970 \n",
"394 -1.283395 1.760831 -0.728146 0.613214 0.534818 0.482541 0.497303 \n",
"831 -1.230133 -0.572774 -0.868328 0.372532 -2.065409 -1.925431 1.802943 \n",
"651 -0.647903 -0.439082 -1.170327 -0.407629 1.252680 -0.108644 0.227973 \n",
"\n",
" X0 X1 \n",
"640 0.118324 -0.659106 \n",
"827 0.748232 0.613970 \n",
"673 0.857110 -0.649783 \n",
"284 0.515098 -0.577574 \n",
"295 1.300355 0.165130 \n",
".. ... ... \n",
"178 0.408551 -1.038557 \n",
"723 0.538970 -0.348836 \n",
"394 0.497303 0.482541 \n",
"831 1.802943 -1.925431 \n",
"651 0.227973 -0.108644 \n",
"\n",
"[800 rows x 9 columns], 'y': 640 3.839554\n",
"827 17.063845\n",
"673 -5.059295\n",
"284 2.864562\n",
"295 9.933260\n",
" ... \n",
"178 6.014561\n",
"723 12.592456\n",
"394 12.331256\n",
"831 4.497600\n",
"651 10.451031\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 640 True\n",
"827 True\n",
"673 False\n",
"284 True\n",
"295 True\n",
" ... \n",
"178 True\n",
"723 True\n",
"394 True\n",
"831 True\n",
"651 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"235 -0.263945 0.606153 -2.132472 0.508581 -0.376937 0.208302 -1.639252 \n",
"293 -2.019915 -0.918739 -1.767926 -1.403425 -0.740373 -1.576824 0.279021 \n",
"505 -2.479285 0.847818 0.513598 -0.218587 0.793758 -1.587561 1.340198 \n",
"816 -0.262987 0.921280 -0.389069 -0.883456 -2.158824 -1.767588 -0.312548 \n",
"877 -0.359565 0.826991 -0.179359 -1.811258 1.418593 1.405163 -0.075609 \n",
".. ... ... ... ... ... ... ... \n",
"677 -0.926078 0.166307 -2.183101 1.988610 -0.286812 -1.952046 0.131363 \n",
"925 -0.292250 -0.487899 -1.791678 1.743438 -1.451384 -1.451548 -0.034151 \n",
"455 -0.472574 1.272672 -2.489234 0.483832 0.334740 -0.703027 1.687994 \n",
"457 -1.376599 1.461089 -0.718660 1.781923 -2.802867 -1.477809 0.128276 \n",
"665 -2.174730 0.248624 -0.092817 -0.715735 0.210519 -0.742166 -0.077510 \n",
"\n",
" X0 X1 \n",
"235 -1.639252 0.208302 \n",
"293 0.279021 -1.576824 \n",
"505 1.340198 -1.587561 \n",
"816 -0.312548 -1.767588 \n",
"877 -0.075609 1.405163 \n",
".. ... ... \n",
"677 0.131363 -1.952046 \n",
"925 -0.034151 -1.451548 \n",
"455 1.687994 -0.703027 \n",
"457 0.128276 -1.477809 \n",
"665 -0.077510 -0.742166 \n",
"\n",
"[800 rows x 9 columns], 'y': 235 2.634861\n",
"293 -11.609205\n",
"505 8.711479\n",
"816 -0.020121\n",
"877 12.698322\n",
" ... \n",
"677 6.044637\n",
"925 4.053751\n",
"455 15.390761\n",
"457 -8.028104\n",
"665 2.331887\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 235 True\n",
"293 False\n",
"505 True\n",
"816 True\n",
"877 True\n",
" ... \n",
"677 True\n",
"925 True\n",
"455 True\n",
"457 False\n",
"665 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: sMAPE (Control): 0.5751\n",
"INFO:causalml: sMAPE (Treatment): 0.1464\n",
"INFO:causalml: Gini (Control): 0.7218\n",
"INFO:causalml: Gini (Treatment): 0.9944\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0223\n",
"INFO:causalml: RMSE (Treatment): 0.7354\n",
"INFO:causalml: sMAPE (Control): 0.5407\n",
"INFO:causalml: sMAPE (Treatment): 0.1396\n",
"INFO:causalml: Gini (Control): 0.7417\n",
"INFO:causalml: Gini (Treatment): 0.9942\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0009\n",
"INFO:causalml: RMSE (Treatment): 0.7661\n",
"INFO:causalml: sMAPE (Control): 0.5483\n",
"INFO:causalml: sMAPE (Treatment): 0.1548\n",
"INFO:causalml: Gini (Control): 0.7414\n",
"INFO:causalml: Gini (Treatment): 0.9938\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"831 -1.230133 -0.572774 -0.868328 0.372532 -2.065409 -1.925431 1.802943 \n",
"872 -0.675589 1.480966 0.633872 -1.812491 0.279114 -0.021202 0.937137 \n",
"895 -0.459769 -0.017087 -0.860174 0.294297 0.061199 0.406711 0.845962 \n",
"102 -0.418300 -0.557531 -1.665453 1.690287 0.196389 -1.122764 0.983299 \n",
"686 -1.377233 -0.617476 -0.353271 2.000711 -1.831548 0.632213 0.262429 \n",
".. ... ... ... ... ... ... ... \n",
"412 -1.218461 2.898433 -0.737451 -1.217685 0.214579 -1.178889 0.280958 \n",
"908 -0.171439 0.987004 1.139063 -0.049279 -1.404765 -0.687979 -0.500347 \n",
"340 0.375616 2.217136 -0.666164 -0.970808 0.446217 -0.864206 1.763119 \n",
"117 -2.185549 0.899307 -1.971894 -1.174155 -1.218568 -0.519142 1.033019 \n",
"772 -1.631390 0.465915 -1.149010 1.590925 -0.376748 -1.413846 -0.295266 \n",
"\n",
" X0 X1 \n",
"831 1.802943 -1.925431 \n",
"872 0.937137 -0.021202 \n",
"895 0.845962 0.406711 \n",
"102 0.983299 -1.122764 \n",
"686 0.262429 0.632213 \n",
".. ... ... \n",
"412 0.280958 -1.178889 \n",
"908 -0.500347 -0.687979 \n",
"340 1.763119 -0.864206 \n",
"117 1.033019 -0.519142 \n",
"772 -0.295266 -1.413846 \n",
"\n",
"[800 rows x 9 columns], 'y': 831 4.497600\n",
"872 11.901044\n",
"895 12.243751\n",
"102 12.024923\n",
"686 4.432389\n",
" ... \n",
"412 7.872612\n",
"908 4.820482\n",
"340 18.529106\n",
"117 -11.180244\n",
"772 3.250030\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 831 True\n",
"872 True\n",
"895 True\n",
"102 True\n",
"686 True\n",
" ... \n",
"412 True\n",
"908 True\n",
"340 True\n",
"117 False\n",
"772 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"550 -0.456966 -0.476118 0.221099 -0.054011 0.708884 -1.962707 0.617111 \n",
"776 -2.842646 -0.725526 -0.406744 -0.025141 -1.114379 0.538557 1.710464 \n",
"208 -1.829170 0.787526 -0.881231 0.083780 -1.917817 -2.457417 0.754683 \n",
"162 -1.388900 0.882150 -0.922345 0.504856 1.804151 -2.140192 2.410467 \n",
"904 -1.109443 3.107200 -0.951024 -0.079768 1.948789 -1.132518 -0.890302 \n",
".. ... ... ... ... ... ... ... \n",
"184 -1.891342 1.936913 -1.618877 -1.053819 0.021617 -0.044179 -0.895357 \n",
"960 0.148135 -0.231778 0.068080 -1.110248 -3.242602 -0.436255 1.334302 \n",
"281 -1.135111 0.940750 -0.067170 -0.759354 1.096828 -1.630748 0.625826 \n",
"84 -1.231904 0.946563 -0.520574 -1.241545 -1.073605 0.279123 0.201559 \n",
"608 0.655518 2.888275 -1.833586 -1.459779 0.528316 1.313325 -0.562842 \n",
"\n",
" X0 X1 \n",
"550 0.617111 -1.962707 \n",
"776 1.710464 0.538557 \n",
"208 0.754683 -2.457417 \n",
"162 2.410467 -2.140192 \n",
"904 -0.890302 -1.132518 \n",
".. ... ... \n",
"184 -0.895357 -0.044179 \n",
"960 1.334302 -0.436255 \n",
"281 0.625826 -1.630748 \n",
"84 0.201559 0.279123 \n",
"608 -0.562842 1.313325 \n",
"\n",
"[800 rows x 9 columns], 'y': 550 9.910321\n",
"776 -12.340879\n",
"208 -9.928296\n",
"162 17.934588\n",
"904 10.065464\n",
" ... \n",
"184 1.132702\n",
"960 4.643805\n",
"281 9.917053\n",
"84 -7.210816\n",
"608 13.248344\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 550 True\n",
"776 False\n",
"208 False\n",
"162 True\n",
"904 True\n",
" ... \n",
"184 True\n",
"960 True\n",
"281 True\n",
"84 False\n",
"608 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"981 -1.677948 -1.622248 -1.171652 0.349836 -2.213015 1.101661 0.722546 \n",
"252 -0.407608 -0.441935 -1.985984 1.444090 0.083796 -0.670731 0.259822 \n",
"433 -1.782608 1.831915 -0.845111 0.759755 0.586684 -1.953787 0.353037 \n",
"190 -1.284989 0.161720 -0.195371 0.142880 -1.766458 -1.817200 1.122913 \n",
"380 -1.222122 -0.625482 -0.290597 0.559950 -0.992344 -0.491524 -0.854497 \n",
".. ... ... ... ... ... ... ... \n",
"383 -1.820459 0.011031 -1.003686 -0.301110 0.254573 -1.724935 0.863482 \n",
"875 -2.411078 0.159398 0.119580 0.748787 -0.457547 0.313736 -0.560625 \n",
"204 -1.495909 1.711115 -1.457953 0.493295 1.141122 0.318999 2.147456 \n",
"635 -0.309105 1.995839 -1.442744 1.469303 -0.866598 1.284870 1.107725 \n",
"18 -1.721390 0.991295 -1.008932 1.539819 -0.923055 -0.609885 -0.196033 \n",
"\n",
" X0 X1 \n",
"981 0.722546 1.101661 \n",
"252 0.259822 -0.670731 \n",
"433 0.353037 -1.953787 \n",
"190 1.122913 -1.817200 \n",
"380 -0.854497 -0.491524 \n",
".. ... ... \n",
"383 0.863482 -1.724935 \n",
"875 -0.560625 0.313736 \n",
"204 2.147456 0.318999 \n",
"635 1.107725 1.284870 \n",
"18 -0.196033 -0.609885 \n",
"\n",
"[800 rows x 9 columns], 'y': 981 -12.765797\n",
"252 -0.786820\n",
"433 7.710126\n",
"190 3.713965\n",
"380 -0.053237\n",
" ... \n",
"383 5.450868\n",
"875 1.205333\n",
"204 18.498160\n",
"635 15.613398\n",
"18 -5.046192\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 981 False\n",
"252 False\n",
"433 True\n",
"190 True\n",
"380 True\n",
" ... \n",
"383 True\n",
"875 True\n",
"204 True\n",
"635 True\n",
"18 False\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1101\n",
"INFO:causalml: RMSE (Treatment): 0.6556\n",
"INFO:causalml: sMAPE (Control): 0.5635\n",
"INFO:causalml: sMAPE (Treatment): 0.1410\n",
"INFO:causalml: Gini (Control): 0.7584\n",
"INFO:causalml: Gini (Treatment): 0.9964\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0568\n",
"INFO:causalml: RMSE (Treatment): 0.7481\n",
"INFO:causalml: sMAPE (Control): 0.5376\n",
"INFO:causalml: sMAPE (Treatment): 0.1475\n",
"INFO:causalml: Gini (Control): 0.7403\n",
"INFO:causalml: Gini (Treatment): 0.9942\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0602\n",
"INFO:causalml: RMSE (Treatment): 0.7217\n",
"INFO:causalml: sMAPE (Control): 0.5135\n",
"INFO:causalml: sMAPE (Treatment): 0.1480\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"455 -0.472574 1.272672 -2.489234 0.483832 0.334740 -0.703027 1.687994 \n",
"576 -0.258472 1.109831 0.044998 0.470214 0.134336 -1.293955 1.695302 \n",
"236 -0.724433 0.998208 0.238311 -1.291449 -1.941921 0.913822 0.345125 \n",
"687 0.178787 0.595348 -1.587125 1.513139 -0.245915 -0.379845 2.377876 \n",
"192 -0.062807 0.029133 -0.344053 0.098793 -0.395855 -0.545119 0.774679 \n",
".. ... ... ... ... ... ... ... \n",
"65 -2.860295 0.491337 0.307032 0.402665 0.265118 -1.899636 -0.565288 \n",
"983 -1.330248 1.247409 -0.571754 -0.359552 0.349860 -0.123228 -0.031874 \n",
"47 0.154544 1.025493 -1.250332 -1.536762 -0.309219 -0.948781 1.324595 \n",
"269 0.573973 -0.053532 -0.674589 -3.004365 1.021180 -0.146757 0.753673 \n",
"422 -1.830252 0.894077 -0.267716 -0.367326 2.202751 -0.079619 0.119763 \n",
"\n",
" X0 X1 \n",
"455 1.687994 -0.703027 \n",
"576 1.695302 -1.293955 \n",
"236 0.345125 0.913822 \n",
"687 2.377876 -0.379845 \n",
"192 0.774679 -0.545119 \n",
".. ... ... \n",
"65 -0.565288 -1.899636 \n",
"983 -0.031874 -0.123228 \n",
"47 1.324595 -0.948781 \n",
"269 0.753673 -0.146757 \n",
"422 0.119763 -0.079619 \n",
"\n",
"[800 rows x 9 columns], 'y': 455 15.390761\n",
"576 16.021321\n",
"236 4.765758\n",
"687 19.701253\n",
"192 10.887872\n",
" ... \n",
"65 -0.860515\n",
"983 7.361146\n",
"47 11.849394\n",
"269 0.249321\n",
"422 11.141529\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 455 True\n",
"576 True\n",
"236 True\n",
"687 True\n",
"192 True\n",
" ... \n",
"65 True\n",
"983 True\n",
"47 True\n",
"269 False\n",
"422 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"143 -2.040739 1.654124 2.203868 1.326991 -0.370663 -1.606991 -0.643100 \n",
"627 -1.523690 -0.192391 1.149614 -0.762336 -0.680800 -0.533576 -0.097514 \n",
"890 -1.222633 2.135828 -0.734609 -0.488569 -0.600416 -0.479990 -0.460961 \n",
"378 -1.938741 0.891352 0.002345 1.742060 -0.104084 -0.598398 0.006701 \n",
"451 -0.565758 1.128663 -0.324990 -0.337899 -0.015063 0.346414 0.109342 \n",
".. ... ... ... ... ... ... ... \n",
"513 -0.719556 1.594492 -0.676926 2.386985 3.196747 -0.287512 0.591108 \n",
"111 1.024268 0.556637 -0.514663 1.858435 -0.414872 -0.567619 0.793652 \n",
"460 -0.386371 1.170936 0.655040 0.226403 -0.871820 -0.967792 1.052236 \n",
"238 -0.166010 0.774921 -0.462897 -0.294769 -1.389314 -0.210174 2.431329 \n",
"206 0.034341 -1.189055 -0.976649 0.149847 1.575362 -0.130203 2.269800 \n",
"\n",
" X0 X1 \n",
"143 -0.643100 -1.606991 \n",
"627 -0.097514 -0.533576 \n",
"890 -0.460961 -0.479990 \n",
"378 0.006701 -0.598398 \n",
"451 0.109342 0.346414 \n",
".. ... ... \n",
"513 0.591108 -0.287512 \n",
"111 0.793652 -0.567619 \n",
"460 1.052236 -0.967792 \n",
"238 2.431329 -0.210174 \n",
"206 2.269800 -0.130203 \n",
"\n",
"[800 rows x 9 columns], 'y': 143 3.430961\n",
"627 2.219546\n",
"890 -3.809211\n",
"378 -2.856631\n",
"451 9.760052\n",
" ... \n",
"513 22.411994\n",
"111 16.735009\n",
"460 11.172108\n",
"238 14.670040\n",
"206 20.680223\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 143 True\n",
"627 True\n",
"890 False\n",
"378 False\n",
"451 True\n",
" ... \n",
"513 True\n",
"111 True\n",
"460 True\n",
"238 True\n",
"206 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Control): 0.7365\n",
"INFO:causalml: Gini (Treatment): 0.9949\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0490\n",
"INFO:causalml: RMSE (Treatment): 0.6879\n",
"INFO:causalml: sMAPE (Control): 0.5267\n",
"INFO:causalml: sMAPE (Treatment): 0.1384\n",
"INFO:causalml: Gini (Control): 0.7423\n",
"INFO:causalml: Gini (Treatment): 0.9953\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9082\n",
"INFO:causalml: RMSE (Treatment): 0.6356\n",
"INFO:causalml: sMAPE (Control): 0.5205\n",
"INFO:causalml: sMAPE (Treatment): 0.1334\n",
"INFO:causalml: Gini (Control): 0.7233\n",
"INFO:causalml: Gini (Treatment): 0.9956\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"808 -0.291951 1.043771 -0.571882 -0.537580 0.895934 -1.203433 1.200730 \n",
"359 -1.250892 0.608337 -1.159236 1.196291 -2.448155 -1.418204 2.171817 \n",
"755 -0.785170 -0.634127 0.249551 -0.613209 0.804813 -1.015528 1.452350 \n",
"656 -1.273620 1.797036 2.092367 0.953316 1.197489 -0.625639 0.731893 \n",
"464 -1.458938 1.066085 -0.038658 -0.458790 0.563506 0.649619 0.007999 \n",
".. ... ... ... ... ... ... ... \n",
"251 -2.083898 -0.553302 0.342455 1.195262 -0.140618 -0.475982 0.394482 \n",
"8 -1.192098 0.543069 0.448059 1.496816 1.148883 -0.101334 0.352319 \n",
"573 0.196341 0.991673 -0.825979 1.860407 -1.870355 -1.152153 -1.051394 \n",
"726 -0.283535 0.621638 -0.999520 0.843142 1.790317 -0.030857 0.902776 \n",
"192 -0.062807 0.029133 -0.344053 0.098793 -0.395855 -0.545119 0.774679 \n",
"\n",
" X0 X1 \n",
"808 1.200730 -1.203433 \n",
"359 2.171817 -1.418204 \n",
"755 1.452350 -1.015528 \n",
"656 0.731893 -0.625639 \n",
"464 0.007999 0.649619 \n",
".. ... ... \n",
"251 0.394482 -0.475982 \n",
"8 0.352319 -0.101334 \n",
"573 -1.051394 -1.152153 \n",
"726 0.902776 -0.030857 \n",
"192 0.774679 -0.545119 \n",
"\n",
"[800 rows x 9 columns], 'y': 808 14.588358\n",
"359 7.556557\n",
"755 12.541520\n",
"656 15.632059\n",
"464 8.601975\n",
" ... \n",
"251 5.436045\n",
"8 13.374297\n",
"573 3.364299\n",
"726 18.301691\n",
"192 10.887872\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 808 True\n",
"359 True\n",
"755 True\n",
"656 True\n",
"464 True\n",
" ... \n",
"251 True\n",
"8 True\n",
"573 True\n",
"726 True\n",
"192 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"121 1.534365 1.840154 -0.218035 -0.257249 -0.734629 -0.767329 -1.295119 \n",
"589 -1.667998 -0.312881 -0.874289 -0.432761 -1.519063 0.481366 -0.232169 \n",
"524 0.057014 0.649900 -0.364445 -0.578270 0.198899 -0.364881 2.339120 \n",
"390 -2.802559 0.829055 -0.457778 0.755228 0.148033 -0.859578 0.281249 \n",
"933 -0.934060 1.319778 0.959669 -0.733035 -1.291453 -0.773287 0.716901 \n",
".. ... ... ... ... ... ... ... \n",
"415 -0.927855 0.937062 0.139474 -0.230930 -1.822390 -1.141240 3.168139 \n",
"351 -0.741034 0.497864 -0.483110 1.193559 -0.155819 -1.606313 0.005075 \n",
"967 -1.386450 1.648145 -0.953457 1.161783 -0.494481 -3.161705 1.259562 \n",
"403 -1.630763 2.405041 0.712370 0.529186 -2.230960 -0.416914 -1.593093 \n",
"966 -2.809966 2.310766 0.855738 2.265568 -1.872551 -0.553294 2.176341 \n",
"\n",
" X0 X1 \n",
"121 -1.295119 -0.767329 \n",
"589 -0.232169 0.481366 \n",
"524 2.339120 -0.364881 \n",
"390 0.281249 -0.859578 \n",
"933 0.716901 -0.773287 \n",
".. ... ... \n",
"415 3.168139 -1.141240 \n",
"351 0.005075 -1.606313 \n",
"967 1.259562 -3.161705 \n",
"403 -1.593093 -0.416914 \n",
"966 2.176341 -0.553294 \n",
"\n",
"[800 rows x 9 columns], 'y': 121 8.541463\n",
"589 -10.258505\n",
"524 18.575304\n",
"390 3.725711\n",
"933 6.639250\n",
" ... \n",
"415 -6.705046\n",
"351 7.214058\n",
"967 -2.657495\n",
"403 -3.265557\n",
"966 9.876889\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 121 True\n",
"589 False\n",
"524 True\n",
"390 True\n",
"933 True\n",
" ... \n",
"415 False\n",
"351 True\n",
"967 False\n",
"403 True\n",
"966 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"190 -1.284989 0.161720 -0.195371 0.142880 -1.766458 -1.817200 1.122913 \n",
"314 -0.846455 -0.409130 -2.466592 0.522989 -0.052613 -2.655562 1.377086 \n",
"42 0.672800 0.403843 -0.485776 1.240526 1.063470 0.870394 0.561993 \n",
"242 -1.740568 1.485447 -2.312406 0.386711 1.155020 -1.148651 -1.499194 \n",
"268 -0.008387 -0.345640 0.297517 0.932659 -0.233741 -0.957095 -1.559348 \n",
".. ... ... ... ... ... ... ... \n",
"731 -0.792085 2.126007 -1.140046 0.187479 -0.140220 -0.338651 1.396477 \n",
"689 -2.243570 1.275778 -1.424485 -2.440678 -1.906424 -2.373430 -0.198568 \n",
"366 -1.403735 1.313386 1.988391 -0.578266 1.442075 1.501325 0.150474 \n",
"179 -1.297348 2.058718 -1.459938 0.264849 0.264363 -1.481147 1.305092 \n",
"237 -0.843070 3.028032 -0.964293 0.319940 -2.199262 -0.535469 -1.279896 \n",
"\n",
" X0 X1 \n",
"190 1.122913 -1.817200 \n",
"314 1.377086 -2.655562 \n",
"42 0.561993 0.870394 \n",
"242 -1.499194 -1.148651 \n",
"268 -1.559348 -0.957095 \n",
".. ... ... \n",
"731 1.396477 -0.338651 \n",
"689 -0.198568 -2.373430 \n",
"366 0.150474 1.501325 \n",
"179 1.305092 -1.481147 \n",
"237 -1.279896 -0.535469 \n",
"\n",
"[800 rows x 9 columns], 'y': 190 3.713965\n",
"314 -3.811119\n",
"42 19.574715\n",
"242 1.894728\n",
"268 3.465358\n",
" ... \n",
"731 13.912472\n",
"689 -13.994008\n",
"366 13.879918\n",
"179 11.624002\n",
"237 -0.336643\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 190 True\n",
"314 False\n",
"42 True\n",
"242 True\n",
"268 True\n",
" ... \n",
"731 True\n",
"689 False\n",
"366 True\n",
"179 True\n",
"237 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9942\n",
"INFO:causalml: RMSE (Treatment): 0.7756\n",
"INFO:causalml: sMAPE (Control): 0.5198\n",
"INFO:causalml: sMAPE (Treatment): 0.1502\n",
"INFO:causalml: Gini (Control): 0.7338\n",
"INFO:causalml: Gini (Treatment): 0.9935\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9713\n",
"INFO:causalml: RMSE (Treatment): 0.6769\n",
"INFO:causalml: sMAPE (Control): 0.5471\n",
"INFO:causalml: sMAPE (Treatment): 0.1401\n",
"INFO:causalml: Gini (Control): 0.7299\n",
"INFO:causalml: Gini (Treatment): 0.9951\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0612\n",
"INFO:causalml: RMSE (Treatment): 0.7682\n",
"INFO:causalml: sMAPE (Control): 0.5225\n",
"INFO:causalml: sMAPE (Treatment): 0.1526\n",
"INFO:causalml: Gini (Control): 0.7297\n",
"INFO:causalml: Gini (Treatment): 0.9939\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"146 -0.706790 -0.165058 -1.348082 0.313898 0.513408 -0.992231 -0.989312 \n",
"573 0.196341 0.991673 -0.825979 1.860407 -1.870355 -1.152153 -1.051394 \n",
"314 -0.846455 -0.409130 -2.466592 0.522989 -0.052613 -2.655562 1.377086 \n",
"370 -1.330248 0.617371 -1.086532 0.939956 -1.523047 -0.029272 0.894390 \n",
"228 -0.479531 0.804877 -0.334062 0.447143 0.576800 -0.277841 -0.766331 \n",
".. ... ... ... ... ... ... ... \n",
"169 -0.409555 -0.737805 -3.028388 0.817463 -0.393057 -0.845180 1.640679 \n",
"111 1.024268 0.556637 -0.514663 1.858435 -0.414872 -0.567619 0.793652 \n",
"29 -1.026446 0.591365 0.862837 0.817784 -0.689067 -0.759426 -0.807769 \n",
"948 0.137207 0.807432 0.012233 -0.321381 -1.727172 -0.265376 1.037937 \n",
"525 -1.141040 0.446646 -0.843669 0.808363 0.327886 -0.739442 0.486219 \n",
"\n",
" X0 X1 \n",
"146 -0.989312 -0.992231 \n",
"573 -1.051394 -1.152153 \n",
"314 1.377086 -2.655562 \n",
"370 0.894390 -0.029272 \n",
"228 -0.766331 -0.277841 \n",
".. ... ... \n",
"169 1.640679 -0.845180 \n",
"111 0.793652 -0.567619 \n",
"29 -0.807769 -0.759426 \n",
"948 1.037937 -0.265376 \n",
"525 0.486219 -0.739442 \n",
"\n",
"[800 rows x 9 columns], 'y': 146 3.922772\n",
"573 3.364299\n",
"314 -3.811119\n",
"370 6.533270\n",
"228 8.255998\n",
" ... \n",
"169 -3.755154\n",
"111 16.735009\n",
"29 3.429318\n",
"948 9.814477\n",
"525 9.406466\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 146 True\n",
"573 True\n",
"314 False\n",
"370 True\n",
"228 True\n",
" ... \n",
"169 False\n",
"111 True\n",
"29 True\n",
"948 True\n",
"525 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"834 -0.921188 0.601325 -1.805151 0.607381 0.656701 0.552350 0.119174 \n",
"421 -2.057703 0.279791 -0.650186 -0.436779 -0.108370 0.907991 -1.865056 \n",
"63 -2.000224 0.352174 0.394109 -0.450750 0.766047 -0.466814 2.799461 \n",
"407 -1.290075 0.523591 -0.037169 -0.751136 0.489787 1.070899 0.626482 \n",
"216 -1.185130 1.723057 -2.436984 -0.947938 -0.797647 -1.237892 0.020300 \n",
".. ... ... ... ... ... ... ... \n",
"560 -0.591932 0.047587 -1.357618 -1.008455 -1.341175 0.311592 1.261070 \n",
"7 0.174130 1.506210 -1.549595 0.782615 -1.878625 -0.626575 1.176085 \n",
"370 -1.330248 0.617371 -1.086532 0.939956 -1.523047 -0.029272 0.894390 \n",
"994 0.160794 0.460056 0.691403 0.008455 -0.133095 -1.589448 1.180637 \n",
"962 -0.058529 0.632542 -0.738886 -0.663994 -0.868324 -2.555294 -0.708468 \n",
"\n",
" X0 X1 \n",
"834 0.119174 0.552350 \n",
"421 -1.865056 0.907991 \n",
"63 2.799461 -0.466814 \n",
"407 0.626482 1.070899 \n",
"216 0.020300 -1.237892 \n",
".. ... ... \n",
"560 1.261070 0.311592 \n",
"7 1.176085 -0.626575 \n",
"370 0.894390 -0.029272 \n",
"994 1.180637 -1.589448 \n",
"962 -0.708468 -2.555294 \n",
"\n",
"[800 rows x 9 columns], 'y': 834 10.523346\n",
"421 -2.608507\n",
"63 15.814566\n",
"407 10.716455\n",
"216 2.376140\n",
" ... \n",
"560 -7.215301\n",
"7 -2.646268\n",
"370 6.533270\n",
"994 13.409683\n",
"962 1.353868\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 834 True\n",
"421 True\n",
"63 True\n",
"407 True\n",
"216 True\n",
" ... \n",
"560 False\n",
"7 False\n",
"370 True\n",
"994 True\n",
"962 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.1340\n",
"INFO:causalml: RMSE (Treatment): 0.7060\n",
"INFO:causalml: sMAPE (Control): 0.5439\n",
"INFO:causalml: sMAPE (Treatment): 0.1519\n",
"INFO:causalml: Gini (Control): 0.7274\n",
"INFO:causalml: Gini (Treatment): 0.9950\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"629 -1.087554 1.775480 1.171119 0.254408 0.110085 -0.901117 0.490717 \n",
"168 0.435794 1.361508 -0.020057 -1.105804 -0.948502 -0.816422 1.539983 \n",
"637 -1.283359 0.832725 0.002037 -1.432698 -2.375726 -1.559718 1.057731 \n",
"565 0.489147 1.219821 -1.070019 0.305099 0.624808 -0.830622 1.045480 \n",
"610 -0.711808 1.595194 0.094204 0.005941 -1.302585 -0.669101 1.030998 \n",
".. ... ... ... ... ... ... ... \n",
"897 -0.501089 -1.031997 -1.334448 -0.012652 -0.451207 -0.315850 -0.369898 \n",
"62 -0.387319 0.363055 -3.887119 -0.957211 -0.720283 -1.195020 -0.053329 \n",
"527 -1.914933 -0.160798 -2.443913 0.459466 0.752528 -0.032713 0.745998 \n",
"941 -0.858300 2.689664 0.060630 -0.800292 -0.649474 -0.542851 0.680252 \n",
"391 -0.307159 1.106719 1.338069 -1.242240 0.718523 0.202760 0.500101 \n",
"\n",
" X0 X1 \n",
"629 0.490717 -0.901117 \n",
"168 1.539983 -0.816422 \n",
"637 1.057731 -1.559718 \n",
"565 1.045480 -0.830622 \n",
"610 1.030998 -0.669101 \n",
".. ... ... \n",
"897 -0.369898 -0.315850 \n",
"62 -0.053329 -1.195020 \n",
"527 0.745998 -0.032713 \n",
"941 0.680252 -0.542851 \n",
"391 0.500101 0.202760 \n",
"\n",
"[800 rows x 9 columns], 'y': 629 10.711793\n",
"168 13.539036\n",
"637 -10.850731\n",
"565 17.014565\n",
"610 9.248446\n",
" ... \n",
"897 -4.566144\n",
"62 -5.979631\n",
"527 8.043131\n",
"941 9.630585\n",
"391 13.502571\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 629 True\n",
"168 True\n",
"637 False\n",
"565 True\n",
"610 True\n",
" ... \n",
"897 False\n",
"62 False\n",
"527 True\n",
"941 True\n",
"391 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"384 -0.952465 0.527441 -1.232497 -0.135736 -0.788245 0.164010 0.849034 \n",
"209 -0.133713 0.654669 -1.106384 -0.108285 1.071118 -1.245543 -0.793213 \n",
"339 -0.174635 1.102320 -0.538718 1.353400 -2.411045 -0.088519 -0.223676 \n",
"952 -0.623857 0.509187 -1.613917 -0.527539 -0.021147 -1.638291 1.771485 \n",
"545 -1.156909 1.955408 0.619625 1.010345 0.520459 0.423056 0.779032 \n",
".. ... ... ... ... ... ... ... \n",
"839 -0.294596 0.983148 -0.666509 0.796590 0.072181 -0.815520 0.188710 \n",
"10 0.846619 2.980613 0.205696 2.969570 0.858587 -1.222764 1.147817 \n",
"734 -2.534543 1.297053 -0.227526 -0.605377 -1.703765 -0.280342 0.415044 \n",
"941 -0.858300 2.689664 0.060630 -0.800292 -0.649474 -0.542851 0.680252 \n",
"350 -0.426754 1.054619 1.005346 0.254238 -0.790041 -1.571358 0.573671 \n",
"\n",
" X0 X1 \n",
"384 0.849034 0.164010 \n",
"209 -0.793213 -1.245543 \n",
"339 -0.223676 -0.088519 \n",
"952 1.771485 -1.638291 \n",
"545 0.779032 0.423056 \n",
".. ... ... \n",
"839 0.188710 -0.815520 \n",
"10 1.147817 -1.222764 \n",
"734 0.415044 -0.280342 \n",
"941 0.680252 -0.542851 \n",
"350 0.573671 -1.571358 \n",
"\n",
"[800 rows x 9 columns], 'y': 384 -5.168611\n",
"209 8.004465\n",
"339 4.815990\n",
"952 -2.868485\n",
"545 15.065277\n",
" ... \n",
"839 10.718157\n",
"10 24.500212\n",
"734 -0.213830\n",
"941 9.630585\n",
"350 8.901755\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 384 False\n",
"209 True\n",
"339 True\n",
"952 False\n",
"545 True\n",
" ... \n",
"839 True\n",
"10 True\n",
"734 True\n",
"941 True\n",
"350 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Control): 3.0399\n",
"INFO:causalml: RMSE (Treatment): 0.7060\n",
"INFO:causalml: sMAPE (Control): 0.5135\n",
"INFO:causalml: sMAPE (Treatment): 0.1434\n",
"INFO:causalml: Gini (Control): 0.7466\n",
"INFO:causalml: Gini (Treatment): 0.9947\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9668\n",
"INFO:causalml: RMSE (Treatment): 0.6953\n",
"INFO:causalml: sMAPE (Control): 0.5294\n",
"INFO:causalml: sMAPE (Treatment): 0.1350\n",
"INFO:causalml: Gini (Control): 0.7468\n",
"INFO:causalml: Gini (Treatment): 0.9948\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0586\n",
"INFO:causalml: RMSE (Treatment): 0.6825\n",
"INFO:causalml: sMAPE (Control): 0.5465\n",
"INFO:causalml: sMAPE (Treatment): 0.1460\n",
"INFO:causalml: Gini (Control): 0.7248\n",
"INFO:causalml: Gini (Treatment): 0.9953\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"907 -0.596246 0.342098 0.422666 1.205834 -0.189522 -0.157053 1.625369 \n",
"110 -1.807733 2.432036 -0.421479 1.445365 -0.460025 0.868047 0.873665 \n",
"282 -0.070319 -0.099650 0.457073 1.593983 -1.547777 -1.467975 0.444530 \n",
"35 -0.572119 -0.757998 -0.095580 -0.836271 0.268984 -1.592530 -0.137505 \n",
"562 -1.449613 1.071250 -1.609878 0.729917 1.114471 1.063556 -0.186103 \n",
".. ... ... ... ... ... ... ... \n",
"825 -1.429357 -0.888809 0.582119 -0.687709 -0.446310 -0.158307 0.811902 \n",
"596 -1.135855 0.142313 0.627392 0.540314 0.120423 -1.359658 2.273243 \n",
"293 -2.019915 -0.918739 -1.767926 -1.403425 -0.740373 -1.576824 0.279021 \n",
"641 -2.239311 2.194117 -0.353902 0.360330 0.632691 -1.221853 -0.388297 \n",
"516 -0.316795 2.424823 0.020510 -0.536910 0.983380 -0.880608 1.127091 \n",
"\n",
" X0 X1 \n",
"907 1.625369 -0.157053 \n",
"110 0.873665 0.868047 \n",
"282 0.444530 -1.467975 \n",
"35 -0.137505 -1.592530 \n",
"562 -0.186103 1.063556 \n",
".. ... ... \n",
"825 0.811902 -0.158307 \n",
"596 2.273243 -1.359658 \n",
"293 0.279021 -1.576824 \n",
"641 -0.388297 -1.221853 \n",
"516 1.127091 -0.880608 \n",
"\n",
"[800 rows x 9 columns], 'y': 907 15.569400\n",
"110 11.934711\n",
"282 7.672536\n",
"35 -2.892738\n",
"562 10.482337\n",
" ... \n",
"825 5.892887\n",
"596 14.773593\n",
"293 -11.609205\n",
"641 4.862547\n",
"516 16.717229\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 907 True\n",
"110 True\n",
"282 True\n",
"35 False\n",
"562 True\n",
" ... \n",
"825 True\n",
"596 True\n",
"293 False\n",
"641 True\n",
"516 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"503 -2.066140 2.033010 -1.559405 0.352297 0.418520 -0.333911 1.084480 \n",
"527 -1.914933 -0.160798 -2.443913 0.459466 0.752528 -0.032713 0.745998 \n",
"65 -2.860295 0.491337 0.307032 0.402665 0.265118 -1.899636 -0.565288 \n",
"143 -2.040739 1.654124 2.203868 1.326991 -0.370663 -1.606991 -0.643100 \n",
"808 -0.291951 1.043771 -0.571882 -0.537580 0.895934 -1.203433 1.200730 \n",
".. ... ... ... ... ... ... ... \n",
"351 -0.741034 0.497864 -0.483110 1.193559 -0.155819 -1.606313 0.005075 \n",
"494 -1.309272 0.323194 -1.405781 0.614937 1.179140 0.560715 -0.047731 \n",
"579 -0.385689 0.379145 0.280441 0.467014 -1.190009 -0.877053 -0.634747 \n",
"220 0.757100 1.090061 -0.251709 0.571761 -1.084509 -1.752894 0.512210 \n",
"336 -3.093855 1.469971 0.367279 1.174817 -0.729766 -2.114001 0.885400 \n",
"\n",
" X0 X1 \n",
"503 1.084480 -0.333911 \n",
"527 0.745998 -0.032713 \n",
"65 -0.565288 -1.899636 \n",
"143 -0.643100 -1.606991 \n",
"808 1.200730 -1.203433 \n",
".. ... ... \n",
"351 0.005075 -1.606313 \n",
"494 -0.047731 0.560715 \n",
"579 -0.634747 -0.877053 \n",
"220 0.512210 -1.752894 \n",
"336 0.885400 -2.114001 \n",
"\n",
"[800 rows x 9 columns], 'y': 503 -3.163402\n",
"527 8.043131\n",
"65 -0.860515\n",
"143 3.430961\n",
"808 14.588358\n",
" ... \n",
"351 7.214058\n",
"494 10.082742\n",
"579 3.518524\n",
"220 10.878908\n",
"336 2.926169\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 503 False\n",
"527 True\n",
"65 True\n",
"143 True\n",
"808 True\n",
" ... \n",
"351 True\n",
"494 True\n",
"579 True\n",
"220 True\n",
"336 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0257\n",
"INFO:causalml: RMSE (Treatment): 0.6446\n",
"INFO:causalml: sMAPE (Control): 0.5649\n",
"INFO:causalml: sMAPE (Treatment): 0.1381\n",
"INFO:causalml: Gini (Control): 0.7243\n",
"INFO:causalml: Gini (Treatment): 0.9956\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"625 -1.336550 0.261883 0.557248 -0.069210 1.161863 -0.761143 1.075224 \n",
"760 -1.539521 0.393619 0.922067 -2.071477 -0.370860 -1.118111 1.021422 \n",
"78 -1.150653 0.133818 0.632773 1.970008 -2.371912 -0.758077 0.049079 \n",
"49 -0.982952 2.225363 -1.526931 -0.694241 0.029243 -0.455032 0.234971 \n",
"75 -0.681840 -0.860902 -0.003540 0.004867 -0.711607 1.445133 2.210988 \n",
".. ... ... ... ... ... ... ... \n",
"7 0.174130 1.506210 -1.549595 0.782615 -1.878625 -0.626575 1.176085 \n",
"710 -0.753746 2.934628 -1.147550 -0.196935 -0.921997 -0.625230 -0.292340 \n",
"943 -2.252890 0.804815 -1.189588 0.399429 -1.924218 -1.450234 0.482038 \n",
"271 -1.683731 -0.490214 1.077274 1.927335 -0.447638 1.027982 -0.317068 \n",
"891 -0.294883 -0.574886 -1.010901 1.587116 0.836384 0.457966 2.071495 \n",
"\n",
" X0 X1 \n",
"625 1.075224 -0.761143 \n",
"760 1.021422 -1.118111 \n",
"78 0.049079 -0.758077 \n",
"49 0.234971 -0.455032 \n",
"75 2.210988 1.445133 \n",
".. ... ... \n",
"7 1.176085 -0.626575 \n",
"710 -0.292340 -0.625230 \n",
"943 0.482038 -1.450234 \n",
"271 -0.317068 1.027982 \n",
"891 2.071495 0.457966 \n",
"\n",
"[800 rows x 9 columns], 'y': 625 12.619755\n",
"760 -7.109575\n",
"78 2.567970\n",
"49 8.274390\n",
"75 -4.842126\n",
" ... \n",
"7 -2.646268\n",
"710 5.904376\n",
"943 -10.951371\n",
"271 6.385709\n",
"891 20.166272\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 625 True\n",
"760 False\n",
"78 True\n",
"49 True\n",
"75 False\n",
" ... \n",
"7 False\n",
"710 True\n",
"943 False\n",
"271 True\n",
"891 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"906 -1.727409 0.768134 0.127817 2.012317 -0.001440 0.690172 0.662514 \n",
"98 -0.724707 -0.167508 -2.200046 1.316365 -0.652669 1.877820 0.372580 \n",
"983 -1.330248 1.247409 -0.571754 -0.359552 0.349860 -0.123228 -0.031874 \n",
"497 -1.333797 1.734853 -0.414480 0.677529 -1.691099 -1.330015 0.657088 \n",
"583 -0.820218 -0.945137 -1.568877 -0.082810 0.614237 -0.346996 0.919443 \n",
".. ... ... ... ... ... ... ... \n",
"894 -0.029186 0.537780 -2.631535 0.408287 -0.885739 -0.202335 1.614458 \n",
"780 -0.483565 0.853663 1.381569 -0.538695 -0.040407 -0.585683 0.004936 \n",
"117 -2.185549 0.899307 -1.971894 -1.174155 -1.218568 -0.519142 1.033019 \n",
"848 -1.892578 1.453947 -2.287147 0.205164 -0.295408 -1.312249 0.506585 \n",
"258 -1.776837 0.805692 -1.001388 0.520858 0.798608 -1.470144 0.534863 \n",
"\n",
" X0 X1 \n",
"906 0.662514 0.690172 \n",
"98 0.372580 1.877820 \n",
"983 -0.031874 -0.123228 \n",
"497 0.657088 -1.330015 \n",
"583 0.919443 -0.346996 \n",
".. ... ... \n",
"894 1.614458 -0.202335 \n",
"780 0.004936 -0.585683 \n",
"117 1.033019 -0.519142 \n",
"848 0.506585 -1.312249 \n",
"258 0.534863 -1.470144 \n",
"\n",
"[800 rows x 9 columns], 'y': 906 11.554534\n",
"98 -3.658929\n",
"983 7.361146\n",
"497 -5.843257\n",
"583 -2.819370\n",
" ... \n",
"894 -2.776312\n",
"780 8.820757\n",
"117 -11.180244\n",
"848 -5.747920\n",
"258 8.016723\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 906 True\n",
"98 False\n",
"983 True\n",
"497 False\n",
"583 False\n",
" ... \n",
"894 False\n",
"780 True\n",
"117 False\n",
"848 False\n",
"258 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0735\n",
"INFO:causalml: RMSE (Treatment): 0.7186\n",
"INFO:causalml: sMAPE (Control): 0.5432\n",
"INFO:causalml: sMAPE (Treatment): 0.1394\n",
"INFO:causalml: Gini (Control): 0.7331\n",
"INFO:causalml: Gini (Treatment): 0.9948\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0731\n",
"INFO:causalml: RMSE (Treatment): 0.7347\n",
"INFO:causalml: sMAPE (Control): 0.5509\n",
"INFO:causalml: sMAPE (Treatment): 0.1434\n",
"INFO:causalml: Gini (Control): 0.7232\n",
"INFO:causalml: Gini (Treatment): 0.9945\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0618\n",
"INFO:causalml: RMSE (Treatment): 0.7741\n",
"INFO:causalml: sMAPE (Control): 0.5257\n",
"INFO:causalml: sMAPE (Treatment): 0.1482\n",
"INFO:causalml: Gini (Control): 0.7235\n",
"INFO:causalml: Gini (Treatment): 0.9937\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"379 -2.642522 2.168664 -0.282198 -0.922232 -0.027263 -1.608540 0.585088 \n",
"91 -1.781143 1.133039 -0.366381 -1.016497 0.753451 -0.648109 0.902865 \n",
"237 -0.843070 3.028032 -0.964293 0.319940 -2.199262 -0.535469 -1.279896 \n",
"521 -1.235550 2.140683 -2.152255 0.310299 -1.837627 -0.705293 -0.465131 \n",
"958 -0.495343 0.847053 -0.028871 -0.718100 -0.871586 -0.630299 -0.132512 \n",
".. ... ... ... ... ... ... ... \n",
"834 -0.921188 0.601325 -1.805151 0.607381 0.656701 0.552350 0.119174 \n",
"323 -1.225390 1.130518 -1.096882 -0.350487 -0.604044 0.001088 0.390490 \n",
"127 -1.184442 1.633850 -0.489806 0.806896 -0.654125 -0.324203 -1.297638 \n",
"591 -1.762898 1.105254 1.394248 0.278984 -0.431230 -0.144092 -0.668399 \n",
"768 -0.693216 0.942545 -0.176337 -1.216868 -1.459316 0.118780 0.014068 \n",
"\n",
" X0 X1 \n",
"379 0.585088 -1.608540 \n",
"91 0.902865 -0.648109 \n",
"237 -1.279896 -0.535469 \n",
"521 -0.465131 -0.705293 \n",
"958 -0.132512 -0.630299 \n",
".. ... ... \n",
"834 0.119174 0.552350 \n",
"323 0.390490 0.001088 \n",
"127 -1.297638 -0.324203 \n",
"591 -0.668399 -0.144092 \n",
"768 0.014068 0.118780 \n",
"\n",
"[800 rows x 9 columns], 'y': 379 -6.731177\n",
"91 9.048832\n",
"237 -0.336643\n",
"521 0.541061\n",
"958 5.051010\n",
" ... \n",
"834 10.523346\n",
"323 6.435152\n",
"127 2.218793\n",
"591 3.407731\n",
"768 3.776833\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 379 False\n",
"91 True\n",
"237 True\n",
"521 True\n",
"958 True\n",
" ... \n",
"834 True\n",
"323 True\n",
"127 True\n",
"591 True\n",
"768 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"66 -0.074892 -0.353977 0.148845 -0.275548 -0.150470 0.507088 -0.258207 \n",
"411 -0.546985 0.632225 0.177670 1.152931 -1.341671 -1.043610 0.915882 \n",
"714 0.484495 0.681690 -1.070190 1.380717 0.003125 -0.471048 -0.511469 \n",
"697 -0.735466 0.746269 -1.122905 0.464823 -0.708806 -2.491421 -0.071736 \n",
"359 -1.250892 0.608337 -1.159236 1.196291 -2.448155 -1.418204 2.171817 \n",
".. ... ... ... ... ... ... ... \n",
"883 -1.053935 -0.020818 -0.218070 -0.163484 -1.436398 0.888213 0.560956 \n",
"677 -0.926078 0.166307 -2.183101 1.988610 -0.286812 -1.952046 0.131363 \n",
"336 -3.093855 1.469971 0.367279 1.174817 -0.729766 -2.114001 0.885400 \n",
"74 -0.996694 0.160173 -2.637286 -2.023282 1.303478 -1.990375 1.207325 \n",
"14 -2.078772 1.196831 1.691383 -0.112157 -0.733879 -1.146159 0.723966 \n",
"\n",
" X0 X1 \n",
"66 -0.258207 0.507088 \n",
"411 0.915882 -1.043610 \n",
"714 -0.511469 -0.471048 \n",
"697 -0.071736 -2.491421 \n",
"359 2.171817 -1.418204 \n",
".. ... ... \n",
"883 0.560956 0.888213 \n",
"677 0.131363 -1.952046 \n",
"336 0.885400 -2.114001 \n",
"74 1.207325 -1.990375 \n",
"14 0.723966 -1.146159 \n",
"\n",
"[800 rows x 9 columns], 'y': 66 8.421138\n",
"411 9.164325\n",
"714 10.878215\n",
"697 3.459264\n",
"359 7.556557\n",
" ... \n",
"883 5.901248\n",
"677 6.044637\n",
"336 2.926169\n",
"74 8.688916\n",
"14 5.340331\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 66 True\n",
"411 True\n",
"714 True\n",
"697 True\n",
"359 True\n",
" ... \n",
"883 True\n",
"677 True\n",
"336 True\n",
"74 True\n",
"14 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0681\n",
"INFO:causalml: RMSE (Treatment): 0.7303\n",
"INFO:causalml: sMAPE (Control): 0.5512\n",
"INFO:causalml: sMAPE (Treatment): 0.1448\n",
"INFO:causalml: Gini (Control): 0.7432\n",
"INFO:causalml: Gini (Treatment): 0.9948\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9306\n",
"INFO:causalml: RMSE (Treatment): 0.6556\n",
"INFO:causalml: sMAPE (Control): 0.5388\n",
"INFO:causalml: sMAPE (Treatment): 0.1402\n",
"INFO:causalml: Gini (Control): 0.7361\n",
"INFO:causalml: Gini (Treatment): 0.9956\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"900 -1.528877 0.854363 -0.020892 -0.125090 1.283825 0.408115 -0.168281 \n",
"446 0.595650 0.365987 -0.116439 0.995399 0.154264 1.021106 -2.210810 \n",
"699 -1.175823 0.350680 0.033996 0.794509 -0.475092 -2.358432 0.259472 \n",
"70 -0.652798 1.861460 -2.041953 -1.200137 0.846213 -0.817692 1.894073 \n",
"933 -0.934060 1.319778 0.959669 -0.733035 -1.291453 -0.773287 0.716901 \n",
".. ... ... ... ... ... ... ... \n",
"578 -0.125015 -0.484719 -0.554158 -0.644919 -0.352951 -0.169403 0.807828 \n",
"365 -0.079718 -0.263842 0.747338 0.929554 0.426811 -1.590757 1.366447 \n",
"765 -0.608825 1.705784 0.327906 -0.288734 -0.692755 1.172959 0.656402 \n",
"607 -0.713550 -0.806284 -0.339457 -0.279553 -0.477920 -0.690686 -0.221325 \n",
"95 -0.058838 4.762058 0.823520 0.244461 -0.340888 -2.698332 -0.572900 \n",
"\n",
" X0 X1 \n",
"900 -0.168281 0.408115 \n",
"446 -2.210810 1.021106 \n",
"699 0.259472 -2.358432 \n",
"70 1.894073 -0.817692 \n",
"933 0.716901 -0.773287 \n",
".. ... ... \n",
"578 0.807828 -0.169403 \n",
"365 1.366447 -1.590757 \n",
"765 0.656402 1.172959 \n",
"607 -0.221325 -0.690686 \n",
"95 -0.572900 -2.698332 \n",
"\n",
"[800 rows x 9 columns], 'y': 900 9.560536\n",
"446 6.907692\n",
"699 4.759513\n",
"70 15.668385\n",
"933 6.639250\n",
" ... \n",
"578 -2.946371\n",
"365 15.195618\n",
"765 11.910646\n",
"607 3.571733\n",
"95 9.600613\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 900 True\n",
"446 True\n",
"699 True\n",
"70 True\n",
"933 True\n",
" ... \n",
"578 False\n",
"365 True\n",
"765 True\n",
"607 True\n",
"95 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"65 -2.860295 0.491337 0.307032 0.402665 0.265118 -1.899636 -0.565288 \n",
"670 -0.325648 -0.215383 -0.051446 0.066123 0.154725 -0.186235 -0.145738 \n",
"829 -1.396963 -0.080609 -2.047189 -0.910429 -1.164537 -1.251936 -0.347075 \n",
"684 -2.035070 2.267671 0.292658 0.472886 1.087179 -0.826693 0.637336 \n",
"959 -1.282991 1.134722 -1.881201 0.830477 0.965414 1.414359 -1.140110 \n",
".. ... ... ... ... ... ... ... \n",
"355 -0.326494 2.433717 0.080853 1.915810 -0.164674 0.466589 -0.152236 \n",
"35 -0.572119 -0.757998 -0.095580 -0.836271 0.268984 -1.592530 -0.137505 \n",
"314 -0.846455 -0.409130 -2.466592 0.522989 -0.052613 -2.655562 1.377086 \n",
"383 -1.820459 0.011031 -1.003686 -0.301110 0.254573 -1.724935 0.863482 \n",
"104 -1.505922 2.182535 -1.410477 1.379506 -1.351655 -1.104962 -1.837318 \n",
"\n",
" X0 X1 \n",
"65 -0.565288 -1.899636 \n",
"670 -0.145738 -0.186235 \n",
"829 -0.347075 -1.251936 \n",
"684 0.637336 -0.826693 \n",
"959 -1.140110 1.414359 \n",
".. ... ... \n",
"355 -0.152236 0.466589 \n",
"35 -0.137505 -1.592530 \n",
"314 1.377086 -2.655562 \n",
"383 0.863482 -1.724935 \n",
"104 -1.837318 -1.104962 \n",
"\n",
"[800 rows x 9 columns], 'y': 65 -0.860515\n",
"670 8.500958\n",
"829 -9.531238\n",
"684 11.451222\n",
"959 7.562645\n",
" ... \n",
"355 13.712619\n",
"35 -2.892738\n",
"314 -3.811119\n",
"383 5.450868\n",
"104 -2.668188\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 65 True\n",
"670 True\n",
"829 False\n",
"684 True\n",
"959 True\n",
" ... \n",
"355 True\n",
"35 False\n",
"314 False\n",
"383 True\n",
"104 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0845\n",
"INFO:causalml: RMSE (Treatment): 0.7984\n",
"INFO:causalml: sMAPE (Control): 0.5368\n",
"INFO:causalml: sMAPE (Treatment): 0.1555\n",
"INFO:causalml: Gini (Control): 0.7325\n",
"INFO:causalml: Gini (Treatment): 0.9935\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"498 0.922735 1.290530 0.397031 0.589139 -0.007310 0.062392 0.668905 \n",
"461 -1.364660 -1.842826 0.241333 0.373454 -0.367738 0.860921 0.880397 \n",
"213 -0.961749 1.214165 -0.354784 1.375875 -1.675979 -0.633450 0.130199 \n",
"280 -0.924446 -0.495998 -1.067446 -0.475160 -0.560881 -1.593906 1.057462 \n",
"872 -0.675589 1.480966 0.633872 -1.812491 0.279114 -0.021202 0.937137 \n",
".. ... ... ... ... ... ... ... \n",
"583 -0.820218 -0.945137 -1.568877 -0.082810 0.614237 -0.346996 0.919443 \n",
"692 -1.425142 -0.090768 -2.394217 0.281658 -1.069567 -1.236345 -1.514344 \n",
"824 -2.113843 0.954802 -0.116011 0.031653 0.087367 -0.731206 1.268051 \n",
"319 -0.246796 1.042340 0.473929 -1.882334 -1.252649 -0.129804 -2.165228 \n",
"968 -0.626450 0.610291 -0.847638 0.077436 0.480144 -1.839511 0.703036 \n",
"\n",
" X0 X1 \n",
"498 0.668905 0.062392 \n",
"461 0.880397 0.860921 \n",
"213 0.130199 -0.633450 \n",
"280 1.057462 -1.593906 \n",
"872 0.937137 -0.021202 \n",
".. ... ... \n",
"583 0.919443 -0.346996 \n",
"692 -1.514344 -1.236345 \n",
"824 1.268051 -0.731206 \n",
"319 -2.165228 -0.129804 \n",
"968 0.703036 -1.839511 \n",
"\n",
"[800 rows x 9 columns], 'y': 498 17.581110\n",
"461 -6.434741\n",
"213 5.306177\n",
"280 5.994137\n",
"872 11.901044\n",
" ... \n",
"583 -2.819370\n",
"692 -8.079772\n",
"824 8.772335\n",
"319 -2.951778\n",
"968 10.038489\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 498 True\n",
"461 False\n",
"213 True\n",
"280 True\n",
"872 True\n",
" ... \n",
"583 False\n",
"692 False\n",
"824 True\n",
"319 True\n",
"968 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"566 -0.600353 1.068704 -1.045374 0.760096 -0.036793 -1.155525 0.884924 \n",
"726 -0.283535 0.621638 -0.999520 0.843142 1.790317 -0.030857 0.902776 \n",
"272 -3.198219 0.554460 0.179309 2.032221 -0.401181 -1.528464 1.415503 \n",
"356 -1.087973 1.063452 -0.582690 -0.391019 -0.717769 0.567377 0.968256 \n",
"836 -0.185908 0.768699 -0.919715 -1.103215 -1.382410 -0.970128 -0.209856 \n",
".. ... ... ... ... ... ... ... \n",
"644 0.074273 -0.584974 -0.371922 0.791947 -2.236616 0.159478 1.327895 \n",
"803 0.354704 1.494915 0.648519 0.097670 -0.697552 0.274267 0.820103 \n",
"186 -0.858757 3.823995 0.218085 1.742811 -1.001666 -0.271821 1.026534 \n",
"359 -1.250892 0.608337 -1.159236 1.196291 -2.448155 -1.418204 2.171817 \n",
"333 -0.903943 1.303046 -0.373566 0.414038 0.837871 -0.085945 0.009761 \n",
"\n",
" X0 X1 \n",
"566 0.884924 -1.155525 \n",
"726 0.902776 -0.030857 \n",
"272 1.415503 -1.528464 \n",
"356 0.968256 0.567377 \n",
"836 -0.209856 -0.970128 \n",
".. ... ... \n",
"644 1.327895 0.159478 \n",
"803 0.820103 0.274267 \n",
"186 1.026534 -0.271821 \n",
"359 2.171817 -1.418204 \n",
"333 0.009761 -0.085945 \n",
"\n",
"[800 rows x 9 columns], 'y': 566 11.504404\n",
"726 18.301691\n",
"272 -7.247960\n",
"356 -4.713297\n",
"836 2.849093\n",
" ... \n",
"644 9.476615\n",
"803 14.674849\n",
"186 14.738530\n",
"359 7.556557\n",
"333 11.223406\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 566 True\n",
"726 True\n",
"272 False\n",
"356 False\n",
"836 True\n",
" ... \n",
"644 True\n",
"803 True\n",
"186 True\n",
"359 True\n",
"333 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: RMSE (Control): 2.9304\n",
"INFO:causalml: RMSE (Treatment): 0.6669\n",
"INFO:causalml: sMAPE (Control): 0.5375\n",
"INFO:causalml: sMAPE (Treatment): 0.1398\n",
"INFO:causalml: Gini (Control): 0.7641\n",
"INFO:causalml: Gini (Treatment): 0.9957\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9838\n",
"INFO:causalml: RMSE (Treatment): 0.6644\n",
"INFO:causalml: sMAPE (Control): 0.5334\n",
"INFO:causalml: sMAPE (Treatment): 0.1405\n",
"INFO:causalml: Gini (Control): 0.7088\n",
"INFO:causalml: Gini (Treatment): 0.9953\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0108\n",
"INFO:causalml: RMSE (Treatment): 0.6659\n",
"INFO:causalml: sMAPE (Control): 0.5188\n",
"INFO:causalml: sMAPE (Treatment): 0.1441\n",
"INFO:causalml: Gini (Control): 0.7486\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"580 -0.197310 2.008508 -0.347956 0.087102 0.032457 -2.205103 -0.149105 \n",
"22 0.539595 1.318356 0.301499 3.369823 0.047571 1.204919 1.920096 \n",
"87 -1.405182 1.380866 -2.273438 1.029827 0.528251 -0.012064 -0.114884 \n",
"522 0.488747 0.260763 -0.084712 2.012467 0.136177 -0.514404 1.454348 \n",
"844 -0.961302 1.353622 0.111644 1.015867 -2.161583 0.255054 0.858101 \n",
".. ... ... ... ... ... ... ... \n",
"364 -1.078590 1.563574 1.504080 -0.486241 -1.540033 -0.435152 0.956055 \n",
"643 -0.837543 2.690282 -0.956587 1.519004 -3.148478 1.188059 1.128666 \n",
"692 -1.425142 -0.090768 -2.394217 0.281658 -1.069567 -1.236345 -1.514344 \n",
"582 -1.856578 1.519025 -0.354817 1.777607 -0.103456 -0.540906 0.894554 \n",
"535 -0.498853 -0.438894 -0.539694 0.645447 -0.372969 -1.473858 2.897329 \n",
"\n",
" X0 X1 \n",
"580 -0.149105 -2.205103 \n",
"22 1.920096 1.204919 \n",
"87 -0.114884 -0.012064 \n",
"522 1.454348 -0.514404 \n",
"844 0.858101 0.255054 \n",
".. ... ... \n",
"364 0.956055 -0.435152 \n",
"643 1.128666 1.188059 \n",
"692 -1.514344 -1.236345 \n",
"582 0.894554 -0.540906 \n",
"535 2.897329 -1.473858 \n",
"\n",
"[800 rows x 9 columns], 'y': 580 8.433110\n",
"22 25.918390\n",
"87 8.347375\n",
"522 19.196033\n",
"844 -5.710027\n",
" ... \n",
"364 7.693260\n",
"643 9.118838\n",
"692 -8.079772\n",
"582 10.549091\n",
"535 16.319324\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 580 True\n",
"22 True\n",
"87 True\n",
"522 True\n",
"844 False\n",
" ... \n",
"364 True\n",
"643 True\n",
"692 False\n",
"582 True\n",
"535 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"532 -1.066751 1.332414 -0.908307 -0.420243 -0.283136 -1.052984 0.072030 \n",
"995 -2.515948 0.660984 -1.169142 -2.272146 -1.210642 -0.284190 -0.127452 \n",
"816 -0.262987 0.921280 -0.389069 -0.883456 -2.158824 -1.767588 -0.312548 \n",
"335 -1.086530 1.994553 0.585116 -0.719867 -2.473053 1.434212 0.722229 \n",
"533 -1.983985 2.080045 -0.821934 -0.160942 -2.473228 -1.513175 -1.154217 \n",
".. ... ... ... ... ... ... ... \n",
"3 -2.330312 1.552707 -1.004874 1.042000 0.342380 -1.363540 1.515319 \n",
"303 -1.789335 1.176506 0.016001 1.199059 0.834791 -0.485443 0.705215 \n",
"998 -0.107884 0.866378 -1.644002 -0.046340 -1.318515 0.888535 1.662692 \n",
"119 -0.412019 -1.707939 0.133539 0.898397 -0.839980 -0.812084 1.197569 \n",
"613 -1.417773 -0.153505 0.258187 0.222832 -1.176441 -0.629369 1.426766 \n",
"\n",
" X0 X1 \n",
"532 0.072030 -1.052984 \n",
"995 -0.127452 -0.284190 \n",
"816 -0.312548 -1.767588 \n",
"335 0.722229 1.434212 \n",
"533 -1.154217 -1.513175 \n",
".. ... ... \n",
"3 1.515319 -1.363540 \n",
"303 0.705215 -0.485443 \n",
"998 1.662692 0.888535 \n",
"119 1.197569 -0.812084 \n",
"613 1.426766 -0.629369 \n",
"\n",
"[800 rows x 9 columns], 'y': 532 5.559274\n",
"995 -13.283829\n",
"816 -0.020121\n",
"335 6.324474\n",
"533 -6.739186\n",
" ... \n",
"3 10.343818\n",
"303 11.687300\n",
"998 13.321122\n",
"119 9.223485\n",
"613 7.422234\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 532 True\n",
"995 False\n",
"816 True\n",
"335 True\n",
"533 True\n",
" ... \n",
"3 True\n",
"303 True\n",
"998 True\n",
"119 True\n",
"613 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Treatment): 0.9954\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0193\n",
"INFO:causalml: RMSE (Treatment): 0.7014\n",
"INFO:causalml: sMAPE (Control): 0.5388\n",
"INFO:causalml: sMAPE (Treatment): 0.1490\n",
"INFO:causalml: Gini (Control): 0.7211\n",
"INFO:causalml: Gini (Treatment): 0.9949\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"199 -0.887451 -0.781149 -0.243760 -0.813839 -0.018489 -1.809168 1.396317 \n",
"249 -0.735623 0.814306 -1.693160 1.847391 -0.791449 -0.970486 -1.068904 \n",
"75 -0.681840 -0.860902 -0.003540 0.004867 -0.711607 1.445133 2.210988 \n",
"629 -1.087554 1.775480 1.171119 0.254408 0.110085 -0.901117 0.490717 \n",
"719 -1.646363 0.269526 -1.677945 1.596705 -1.646524 0.649971 -0.577936 \n",
".. ... ... ... ... ... ... ... \n",
"503 -2.066140 2.033010 -1.559405 0.352297 0.418520 -0.333911 1.084480 \n",
"123 -0.755355 1.402171 -0.488604 1.421476 -2.454651 1.055428 -1.229143 \n",
"425 -0.547299 1.010457 0.355019 0.509403 -0.299352 -1.751637 1.623652 \n",
"153 -0.787940 1.279546 -1.034601 0.763826 -0.810432 -1.157911 -0.536019 \n",
"180 -0.772968 0.508917 -1.168432 -0.936875 1.844232 -0.008911 0.298550 \n",
"\n",
" X0 X1 \n",
"199 1.396317 -1.809168 \n",
"249 -1.068904 -0.970486 \n",
"75 2.210988 1.445133 \n",
"629 0.490717 -0.901117 \n",
"719 -0.577936 0.649971 \n",
".. ... ... \n",
"503 1.084480 -0.333911 \n",
"123 -1.229143 1.055428 \n",
"425 1.623652 -1.751637 \n",
"153 -0.536019 -1.157911 \n",
"180 0.298550 -0.008911 \n",
"\n",
"[800 rows x 9 columns], 'y': 199 8.221818\n",
"249 -2.008335\n",
"75 -4.842126\n",
"629 10.711793\n",
"719 0.828056\n",
" ... \n",
"503 -3.163402\n",
"123 1.168663\n",
"425 13.323890\n",
"153 3.964156\n",
"180 12.418586\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 199 True\n",
"249 False\n",
"75 False\n",
"629 True\n",
"719 True\n",
" ... \n",
"503 False\n",
"123 True\n",
"425 True\n",
"153 True\n",
"180 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 3.0336\n",
"INFO:causalml: RMSE (Treatment): 0.7137\n",
"INFO:causalml: sMAPE (Control): 0.5254\n",
"INFO:causalml: sMAPE (Treatment): 0.1430\n",
"INFO:causalml: Gini (Control): 0.7298\n",
"INFO:causalml: Gini (Treatment): 0.9946\n",
"INFO:dowhy.causal_estimator:INFO: Using CausalML Estimator\n",
"INFO:dowhy.causal_estimator:b: y~v0+W4+W2+W1+W3+W0+X1+X0\n",
"INFO:causalml:Error metrics for group True\n",
"INFO:causalml: RMSE (Control): 2.9814\n",
"INFO:causalml: RMSE (Treatment): 0.6610\n",
"INFO:causalml: sMAPE (Control): 0.5544\n",
"INFO:causalml: sMAPE (Treatment): 0.1489\n",
"INFO:causalml: Gini (Control): 0.6981\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"772 -1.631390 0.465915 -1.149010 1.590925 -0.376748 -1.413846 -0.295266 \n",
"75 -0.681840 -0.860902 -0.003540 0.004867 -0.711607 1.445133 2.210988 \n",
"897 -0.501089 -1.031997 -1.334448 -0.012652 -0.451207 -0.315850 -0.369898 \n",
"197 -1.572938 0.609204 -2.470786 2.406344 -0.928691 -0.722715 1.007663 \n",
"650 -1.089175 0.031213 -2.627317 1.213359 0.277555 -1.571450 0.129622 \n",
".. ... ... ... ... ... ... ... \n",
"112 0.060647 0.642094 1.051395 1.234630 -0.601046 0.340267 2.262831 \n",
"818 -2.294772 1.081263 -0.110948 0.240523 -2.452061 -0.676310 1.024324 \n",
"646 -0.626251 1.021268 0.186793 1.691995 -1.534219 -0.559809 0.720521 \n",
"720 -0.178107 0.545358 -1.188979 -2.009287 -1.712708 0.183073 2.293672 \n",
"593 -0.197920 1.086551 -0.777233 -1.511006 -0.413144 0.015110 0.344828 \n",
"\n",
" X0 X1 \n",
"772 -0.295266 -1.413846 \n",
"75 2.210988 1.445133 \n",
"897 -0.369898 -0.315850 \n",
"197 1.007663 -0.722715 \n",
"650 0.129622 -1.571450 \n",
".. ... ... \n",
"112 2.262831 0.340267 \n",
"818 1.024324 -0.676310 \n",
"646 0.720521 -0.559809 \n",
"720 2.293672 0.183073 \n",
"593 0.344828 0.015110 \n",
"\n",
"[800 rows x 9 columns], 'y': 772 3.250030\n",
"75 -4.842126\n",
"897 -4.566144\n",
"197 -4.810420\n",
"650 -2.393578\n",
" ... \n",
"112 20.053296\n",
"818 -11.770683\n",
"646 9.399353\n",
"720 11.018665\n",
"593 8.505994\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 772 True\n",
"75 False\n",
"897 False\n",
"197 False\n",
"650 False\n",
" ... \n",
"112 True\n",
"818 False\n",
"646 True\n",
"720 True\n",
"593 True\n",
"Name: v0, Length: 800, dtype: bool}\n",
"{'X': W4 W2 W1 W3 W0 X1 X0 \\\n",
"14 -2.078772 1.196831 1.691383 -0.112157 -0.733879 -1.146159 0.723966 \n",
"309 -0.539594 2.000693 1.298256 0.596046 -2.741050 -0.539698 0.178355 \n",
"185 -0.265206 0.795345 -1.677192 -0.822158 0.585555 -0.521756 -0.615447 \n",
"212 -0.919538 1.656161 0.432204 0.358171 -0.174663 -1.124815 1.438214 \n",
"467 -1.523227 1.571773 0.543944 0.010920 1.160325 -0.031949 0.427831 \n",
".. ... ... ... ... ... ... ... \n",
"475 -1.371406 -1.789706 -1.298206 -1.611236 -0.709442 -0.195347 2.724470 \n",
"248 -0.322096 0.956739 -1.722687 0.445099 1.192956 -0.951015 -0.322611 \n",
"867 -1.435952 0.855195 0.715461 -0.153104 -1.408541 -0.723836 0.896810 \n",
"458 -1.265210 -0.125523 -0.769733 1.467806 -1.007910 0.213974 0.547473 \n",
"711 0.099864 0.069601 -0.148899 0.008564 -0.642494 -0.050277 0.035692 \n",
"\n",
" X0 X1 \n",
"14 0.723966 -1.146159 \n",
"309 0.178355 -0.539698 \n",
"185 -0.615447 -0.521756 \n",
"212 1.438214 -1.124815 \n",
"467 0.427831 -0.031949 \n",
".. ... ... \n",
"475 2.724470 -0.195347 \n",
"248 -0.322611 -0.951015 \n",
"867 0.896810 -0.723836 \n",
"458 0.547473 0.213974 \n",
"711 0.035692 -0.050277 \n",
"\n",
"[800 rows x 9 columns], 'y': 14 5.340331\n",
"309 4.869483\n",
"185 -0.302411\n",
"212 13.226200\n",
"467 12.156180\n",
" ... \n",
"475 -10.586297\n",
"248 10.511960\n",
"867 5.611633\n",
"458 7.125670\n",
"711 8.687897\n",
"Name: y, Length: 800, dtype: float64, 'treatment': 14 True\n",
"309 True\n",
"185 False\n",
"212 True\n",
"467 True\n",
" ... \n",
"475 False\n",
"248 True\n",
"867 True\n",
"458 True\n",
"711 True\n",
"Name: v0, Length: 800, dtype: bool}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:causalml: Gini (Treatment): 0.9955\n",
"INFO:dowhy.causal_refuters.data_subset_refuter:Making use of Bootstrap as we have more than 100 examples.\n",
" Note: The greater the number of examples, the more accurate are the confidence estimates\n"
]
}
],
"source": [
"refutation_list = []\n",
"for estimand in identified_estimands:\n",
" for estimate in estimate_list: \n",
" for refuter in refuter_list:\n",
" ref = model.refute_estimate(estimand, estimate,method_name=refuter)\n",
" refutation_list.append(ref)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refutation Values"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"####### Refutation 1#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"Refute: Bootstrap Sample Dataset\n",
"\n",
"Refute: Bootstrap Sample Dataset\n",
"Estimated effect:12.89367255934708\n",
"New effect:12.374411274559812\n",
"p value:0.28\n",
"\n",
"########################################################################################################\n",
"\n",
"####### Refutation 2#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"Refute: Use a subset of data\n",
"\n",
"Refute: Use a subset of data\n",
"Estimated effect:12.89367255934708\n",
"New effect:12.616442370428834\n",
"p value:0.31000000000000005\n",
"\n",
"########################################################################################################\n",
"\n",
"####### Refutation 3#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"Refute: Bootstrap Sample Dataset\n",
"\n",
"Refute: Bootstrap Sample Dataset\n",
"Estimated effect:12.075122341243496\n",
"New effect:12.111367745920434\n",
"p value:0.44\n",
"\n",
"########################################################################################################\n",
"\n",
"####### Refutation 4#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"Refute: Use a subset of data\n",
"\n",
"Refute: Use a subset of data\n",
"Estimated effect:12.075122341243496\n",
"New effect:12.063766705476722\n",
"p value:0.47\n",
"\n",
"########################################################################################################\n",
"\n",
"####### Refutation 5#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"Refute: Bootstrap Sample Dataset\n",
"\n",
"Refute: Bootstrap Sample Dataset\n",
"Estimated effect:[11.29290641]\n",
"New effect:11.336360019179892\n",
"p value:[0.44]\n",
"\n",
"########################################################################################################\n",
"\n",
"####### Refutation 6#######################################################################################\n",
"*** Class Name ***\n",
"\n",
"Refute: Use a subset of data\n",
"\n",
"Refute: Use a subset of data\n",
"Estimated effect:[11.29290641]\n",
"New effect:11.283369137714823\n",
"p value:[0.47]\n",
"\n",
"########################################################################################################\n",
"\n"
]
}
],
"source": [
"refuter_count = 1\n",
"if inspect_refutations is True:\n",
" for refutation in refutation_list:\n",
" print(\"####### Refutation {}#######################################################################################\".format(refuter_count))\n",
" print(\"*** Class Name ***\")\n",
" print()\n",
" print(refutation.refutation_type)\n",
" print()\n",
" print(refutation)\n",
" print(\"########################################################################################################\")\n",
" print()\n",
" refuter_count += 1"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": true,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}