Source code for dowhy.causal_estimators.propensity_score_estimator

import numpy as np
import pandas as pd

from dowhy.causal_estimator import CausalEstimator

[docs]class PropensityScoreEstimator(CausalEstimator): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # We need to initialize the model when we create any propensity score estimator self._propensity_score_model = None # Check if the treatment is one-dimensional if len(self._treatment_name) > 1: error_msg = str(self.__class__) + "cannot handle more than one treatment variable" raise Exception(error_msg) # Checking if the treatment is binary if not pd.api.types.is_bool_dtype(self._data[self._treatment_name[0]]): error_msg = "Propensity score methods are applicable only for binary treatments" self.logger.error(error_msg) raise Exception(error_msg) self.logger.debug("Back-door variables used:" + ",".join(self._target_estimand.backdoor_variables)) self._observed_common_causes_names = self._target_estimand.backdoor_variables if self._observed_common_causes_names: self._observed_common_causes = self._data[self._observed_common_causes_names] # Convert the categorical variables into dummy/indicator variables # Basically, this gives a one hot encoding for each category # The first category is taken to be the base line. self._observed_common_causes = pd.get_dummies(self._observed_common_causes, drop_first=True) else: self._observed_common_causes = None error_msg = "No common causes/confounders present. Propensity score based methods are not applicable" self.logger.error(error_msg) raise Exception(error_msg)
[docs] def construct_symbolic_estimator(self, estimand): ''' A symbolic string that conveys what each estimator does. For instance, linear regression is expressed as y ~ bx + e ''' raise NotImplementedError
def _estimate_effect(self, recalculate_propensity_score=False): ''' A custom estimator based on the way the propensity score estimates are to be used. Parameters ----------- recalculate_propensity_score: bool, default False, This forces the estimator to recalculate the estimate for the propensity score. ''' raise NotImplementedError