Source code for dowhy.causal_refuters.placebo_treatment_refuter

import copy

import numpy as np
import pandas as pd
import logging 

from dowhy.causal_refuter import CausalRefutation
from dowhy.causal_refuter import CausalRefuter
from dowhy.causal_estimator import CausalEstimator, CausalEstimate

[docs]class PlaceboTreatmentRefuter(CausalRefuter): """Refute an estimate by replacing treatment with a randomly-generated placebo variable. Supports additional parameters that can be specified in the refute_estimate() method. - 'placebo_type': str, None by default Default is to generate random values for the treatment. If placebo_type is "permute", then the original treatment values are permuted by row. - 'num_simulations': int, CausalRefuter.DEFAULT_NUM_SIMULATIONS by default The number of simulations to be run - 'random_state': int, RandomState, None by default The seed value to be added if we wish to repeat the same random behavior. If we want to repeat the same behavior we push the same seed in the psuedo-random generator """ # Default value of the p value taken for the distribution DEFAULT_PROBABILITY_OF_BINOMIAL = 0.5 # Number of Trials: Number of cointosses to understand if a sample gets the treatment DEFAULT_NUMBER_OF_TRIALS = 1 # Mean of the Normal Distribution DEFAULT_MEAN_OF_NORMAL = 0 # Standard Deviation of the Normal Distribution DEFAULT_STD_DEV_OF_NORMAL = 0 def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._placebo_type = kwargs.pop("placebo_type",None) if self._placebo_type is None: self._placebo_type = "Random Data" self._num_simulations = kwargs.pop("num_simulations",CausalRefuter.DEFAULT_NUM_SIMULATIONS) self._random_state = kwargs.pop("random_state",None) if 'logging_level' in kwargs: logging.basicConfig(level=kwargs['logging_level']) else: logging.basicConfig(level=logging.INFO) self.logger = logging.getLogger(__name__)
[docs] def refute_estimate(self): # We need to change the identified estimand # We make a copy as a safety measure, we don't want to change the # original DataFrame identified_estimand = copy.deepcopy(self._target_estimand) identified_estimand.treatment_variable = ["placebo"] sample_estimates = np.zeros(self._num_simulations)"Refutation over {} simulated datasets of {} treatment" .format(self._num_simulations ,self._placebo_type) ) num_rows = self._data.shape[0] treatment_name = self._treatment_name[0] # Extract the name of the treatment variable type_dict = dict( self._data.dtypes ) for index in range(self._num_simulations): if self._placebo_type == "permute": if self._random_state is None: new_treatment = self._data[self._treatment_name].sample(frac=1).values else: new_treatment = self._data[self._treatment_name].sample(frac=1, random_state=self._random_state).values else: if 'float' in type_dict[treatment_name].name :"Using a Normal Distribution with Mean:{} and Variance:{}" .format(PlaceboTreatmentRefuter.DEFAULT_MEAN_OF_NORMAL ,PlaceboTreatmentRefuter.DEFAULT_STD_DEV_OF_NORMAL) ) new_treatment = np.random.randn(num_rows)*PlaceboTreatmentRefuter.DEFAULT_STD_DEV_OF_NORMAL + \ PlaceboTreatmentRefuter.DEFAULT_MEAN_OF_NORMAL elif 'bool' in type_dict[treatment_name].name :"Using a Binomial Distribution with {} trials and {} probability of success" .format(PlaceboTreatmentRefuter.DEFAULT_NUMBER_OF_TRIALS ,PlaceboTreatmentRefuter.DEFAULT_PROBABILITY_OF_BINOMIAL) ) new_treatment = np.random.binomial(PlaceboTreatmentRefuter.DEFAULT_NUMBER_OF_TRIALS, PlaceboTreatmentRefuter.DEFAULT_PROBABILITY_OF_BINOMIAL, num_rows).astype(bool) elif 'int' in type_dict[treatment_name].name :"Using a Discrete Uniform Distribution lying between {} and {}" .format(self._data[treatment_name].min() ,self._data[treatment_name].max()) ) new_treatment = np.random.randint(low=self._data[treatment_name].min(), high=self._data[treatment_name].max(), size=num_rows) elif 'category' in type_dict[treatment_name].name : categories = self._data[treatment_name].unique()"Using a Discrete Uniform Distribution with the following categories:{}" .format(categories)) sample = np.random.choice(categories, size=num_rows) new_treatment = pd.Series(sample).astype('category') # Create a new column in the data by the name of placebo new_data = self._data.assign(placebo=new_treatment) # Sanity check the data self.logger.debug(new_data[0:10]) new_estimator = CausalEstimator.get_estimator_object(new_data, identified_estimand, self._estimate) new_effect = new_estimator.estimate_effect() sample_estimates[index] = new_effect.value refute = CausalRefutation(self._estimate.value, np.mean(sample_estimates), refutation_type="Refute: Use a Placebo Treatment") # Note: We hardcode the estimate value to ZERO as we want to check if it falls in the distribution of the refuter # Ideally we should expect that ZERO should fall in the distribution of the effect estimates as we have severed any causal # relationship between the treatment and the outcome. dummy_estimator = CausalEstimate( estimate = 0, target_estimand =self._estimate.target_estimand, realized_estimand_expr=self._estimate.realized_estimand_expr) refute.add_significance_test_results( self.test_significance(dummy_estimator, sample_estimates) ) return refute