causalml/inference/iv/drivlearner.py [415:433]:
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                te_bootstraps[:, :, i] = te_b

            te_lower = np.percentile(te_bootstraps, (self.ate_alpha / 2) * 100, axis=2)
            te_upper = np.percentile(
                te_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=2
            )

            # 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 (te, te_lower, te_upper)

    def estimate_ate(
        self,
        X,
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causalml/inference/meta/drlearner.py [320:338]:
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                te_bootstraps[:, :, i] = te_b

            te_lower = np.percentile(te_bootstraps, (self.ate_alpha / 2) * 100, axis=2)
            te_upper = np.percentile(
                te_bootstraps, (1 - self.ate_alpha / 2) * 100, axis=2
            )

            # 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 (te, te_lower, te_upper)

    def estimate_ate(
        self,
        X,
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