dowhy/causal_estimators/regression_estimator.py [93:102]:
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            features = np.concatenate((treatment_2d, observed_common_causes_vals),
                                  axis=1)
        else:
            features = treatment_2d
        if self._effect_modifier_names:
            for i in range(treatment_2d.shape[1]):
                curr_treatment = treatment_2d[:,i]
                new_features = curr_treatment[:, np.newaxis] * effect_modifiers_vals.to_numpy()
                features = np.concatenate((features, new_features), axis=1)
        features = features.astype(float, copy=False) # converting to float in case of binary treatment and no other variables
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dowhy/causal_estimators/two_stage_regression_estimator.py [184:193]:
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            features = np.concatenate((treatment_2d, observed_common_causes_vals),
                                  axis=1)
        else:
            features = treatment_2d
        if self._effect_modifier_names:
            for i in range(treatment_2d.shape[1]):
                curr_treatment = treatment_2d[:,i]
                new_features = curr_treatment[:, np.newaxis] * effect_modifiers_vals.to_numpy()
                features = np.concatenate((features, new_features), axis=1)
        features = features.astype(float, copy=False) # converting to float in case of binary treatment and no other variables
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