causalml/inference/iv/drivlearner.py [577:593]:
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                )
                ate_bootstraps[:, n] = cate_b.mean()

            ate_lower = np.percentile(
                ate_bootstraps, (self.ate_alpha / 2) * 100, axis=1
            )
            ate_upper = np.percentile(
                ate_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=1
            )

            # set member variables back to global (currently last bootstrapped outcome)
            self.t_groups = t_groups_global
            self._classes = _classes_global
            self.models_mu_c = deepcopy(models_mu_c_global)
            self.models_mu_t = deepcopy(models_mu_t_global)
            self.models_tau = deepcopy(models_tau_global)
            return ate, ate_lower, ate_upper
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causalml/inference/meta/drlearner.py [436:452]:
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                )
                ate_bootstraps[:, n] = cate_b.mean()

            ate_lower = np.percentile(
                ate_bootstraps, (self.ate_alpha / 2) * 100, axis=1
            )
            ate_upper = np.percentile(
                ate_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=1
            )

            # set member variables back to global (currently last bootstrapped outcome)
            self.t_groups = t_groups_global
            self._classes = _classes_global
            self.models_mu_c = deepcopy(models_mu_c_global)
            self.models_mu_t = deepcopy(models_mu_t_global)
            self.models_tau = deepcopy(models_tau_global)
            return ate, ate_lower, ate_upper
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