causalml/inference/meta/rlearner.py [122:139]:
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        for group in self.t_groups:
            mask = (treatment == group) | (treatment == self.control_name)
            treatment_filt = treatment[mask]
            X_filt = X[mask]
            y_filt = y[mask]
            yhat_filt = yhat[mask]
            p_filt = p[group][mask]
            w = (treatment_filt == group).astype(int)

            if verbose:
                logger.info('training the treatment effect model for {} with R-loss'.format(group))
            self.models_tau[group].fit(X_filt, (y_filt - yhat_filt) / (w - p_filt),
                                       sample_weight=(w - p_filt) ** 2)

            self.vars_c[group] = (y_filt[w == 0] - yhat_filt[w == 0]).var()
            self.vars_t[group] = (y_filt[w == 1] - yhat_filt[w == 1]).var()

    def predict(self, X):
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causalml/inference/meta/rlearner.py [563:580]:
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        for group in self.t_groups:
            mask = (treatment == group) | (treatment == self.control_name)
            treatment_filt = treatment[mask]
            X_filt = X[mask]
            y_filt = y[mask]
            yhat_filt = yhat[mask]
            p_filt = p[group][mask]
            w = (treatment_filt == group).astype(int)

            if verbose:
                logger.info('training the treatment effect model for {} with R-loss'.format(group))
            self.models_tau[group].fit(X_filt, (y_filt - yhat_filt) / (w - p_filt),
                                       sample_weight=(w - p_filt) ** 2)

            self.vars_c[group] = (y_filt[w == 0] - yhat_filt[w == 0]).var()
            self.vars_t[group] = (y_filt[w == 1] - yhat_filt[w == 1]).var()

    def predict(self, X):
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