dowhy/causal_estimators/propensity_score_stratification_estimator.py [49:59]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def _estimate_effect(self):
        if self.recalculate_propensity_score is True:
            if self.propensity_score_model is None:
                self.propensity_score_model = linear_model.LogisticRegression()
            self.propensity_score_model.fit(self._observed_common_causes, self._treatment)
            self._data[self.propensity_score_column] = self.propensity_score_model.predict_proba(self._observed_common_causes)[:, 1]
        else:
            # check if the user provides the propensity score column
            if self.propensity_score_column not in self._data.columns:
                raise ValueError(f"Propensity score column {self.propensity_score_column} does not exist. Please specify the column name that has your pre-computed propensity score.")
            else:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



dowhy/causal_estimators/propensity_score_weighting_estimator.py [55:65]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    def _estimate_effect(self):
        if self.recalculate_propensity_score is True:
            if self.propensity_score_model is None:
                self.propensity_score_model = linear_model.LogisticRegression()
            self.propensity_score_model.fit(self._observed_common_causes, self._treatment)
            self._data[self.propensity_score_column] = self.propensity_score_model.predict_proba(self._observed_common_causes)[:, 1]
        else:
            # check if user provides the propensity score column
            if self.propensity_score_column not in self._data.columns:
                raise ValueError(f"Propensity score column {self.propensity_score_column} does not exist. Please specify the column name that has your pre-computed propensity score.")
            else:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -