causalml/inference/iv/drivlearner.py [296:322]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            ].mean(axis=0)
            yhat_ts[group] = np.r_[
                [model.predict(X) for model in self.models_mu_t[group]]
            ].mean(axis=0)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)
                treatment_filt = treatment[mask]
                X_filt = X[mask]
                y_filt = y[mask]
                w = (treatment_filt == group).astype(int)

                yhat = np.zeros_like(y_filt, dtype=float)
                yhat[w == 0] = yhat_cs[group][mask][w == 0]
                yhat[w == 1] = yhat_ts[group][mask][w == 1]

                logger.info("Error metrics for group {}".format(group))
                regression_metrics(y_filt, yhat, w)

        if not return_components:
            return te
        else:
            return te, yhat_cs, yhat_ts

    def fit_predict(
        self,
        X,
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causalml/inference/meta/drlearner.py [230:256]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
            ].mean(axis=0)
            yhat_ts[group] = np.r_[
                [model.predict(X) for model in self.models_mu_t[group]]
            ].mean(axis=0)

            if (y is not None) and (treatment is not None) and verbose:
                mask = (treatment == group) | (treatment == self.control_name)
                treatment_filt = treatment[mask]
                X_filt = X[mask]
                y_filt = y[mask]
                w = (treatment_filt == group).astype(int)

                yhat = np.zeros_like(y_filt, dtype=float)
                yhat[w == 0] = yhat_cs[group][mask][w == 0]
                yhat[w == 1] = yhat_ts[group][mask][w == 1]

                logger.info("Error metrics for group {}".format(group))
                regression_metrics(y_filt, yhat, w)

        if not return_components:
            return te
        else:
            return te, yhat_cs, yhat_ts

    def fit_predict(
        self,
        X,
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