{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimating effect of multiple treatments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import logging\n",
    "\n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "import dowhy.datasets\n",
    "\n",
    "import econml\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = dowhy.datasets.linear_dataset(10, num_common_causes=4, num_samples=10000,\n",
    "                                    num_instruments=0, num_effect_modifiers=2,\n",
    "                                     num_treatments=2,\n",
    "                                    treatment_is_binary=False,\n",
    "                                    num_discrete_common_causes=2,\n",
    "                                    num_discrete_effect_modifiers=0,\n",
    "                                    one_hot_encode=False)\n",
    "df=data['df']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CausalModel(data=data[\"df\"], \n",
    "                    treatment=data[\"treatment_name\"], outcome=data[\"outcome_name\"], \n",
    "                    graph=data[\"gml_graph\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.view_model()\n",
    "from IPython.display import Image, display\n",
    "display(Image(filename=\"causal_model.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "identified_estimand= model.identify_effect(proceed_when_unidentifiable=True)\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear model\n",
    "\n",
    "Let us first see an example for a linear model. The control_value and treatment_value can be provided as a tuple/list when the treatment is multi-dimensional.\n",
    "\n",
    "The interpretation is change in y when v0 and v1 are changed from (0,0) to (1,1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_estimate = model.estimate_effect(identified_estimand, \n",
    "                                        method_name=\"backdoor.linear_regression\",\n",
    "                                       control_value=(0,0),\n",
    "                                       treatment_value=(1,1),\n",
    "                                       method_params={'need_conditional_estimates': False})\n",
    "print(linear_estimate) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can estimate conditional effects, based on effect modifiers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_estimate = model.estimate_effect(identified_estimand, \n",
    "                                        method_name=\"backdoor.linear_regression\",\n",
    "                                       control_value=(0,0),\n",
    "                                       treatment_value=(1,1))\n",
    "print(linear_estimate) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More methods\n",
    "\n",
    "You can also use methods from EconML or CausalML libraries that support multiple treatments. You can look at examples from the conditional effect notebook: https://microsoft.github.io/dowhy/example_notebooks/dowhy-conditional-treatment-effects.html\n",
    "\n",
    "Propensity-based methods do not support multiple treatments currently. \n"
   ]
  }
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