causalml/inference/iv/drivlearner.py [31:74]:
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    def __init__(
        self,
        learner=None,
        control_outcome_learner=None,
        treatment_outcome_learner=None,
        treatment_effect_learner=None,
        ate_alpha=0.05,
        control_name=0,
    ):
        """Initialize a DR-learner.

        Args:
            learner (optional): a model to estimate outcomes and treatment effects in both the control and treatment
                groups
            control_outcome_learner (optional): a model to estimate outcomes in the control group
            treatment_outcome_learner (optional): a model to estimate outcomes in the treatment group
            treatment_effect_learner (optional): a model to estimate treatment effects in the treatment group. It needs
                to take `sample_weight` as an input argument in `fit()`.
            ate_alpha (float, optional): the confidence level alpha of the ATE estimate
            control_name (str or int, optional): name of control group
        """
        assert (learner is not None) or (
            (control_outcome_learner is not None)
            and (treatment_outcome_learner is not None)
            and (treatment_effect_learner is not None)
        )

        if control_outcome_learner is None:
            self.model_mu_c = deepcopy(learner)
        else:
            self.model_mu_c = control_outcome_learner

        if treatment_outcome_learner is None:
            self.model_mu_t = deepcopy(learner)
        else:
            self.model_mu_t = treatment_outcome_learner

        if treatment_effect_learner is None:
            self.model_tau = deepcopy(learner)
        else:
            self.model_tau = treatment_effect_learner

        self.ate_alpha = ate_alpha
        self.control_name = control_name
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causalml/inference/meta/drlearner.py [31:73]:
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    def __init__(
        self,
        learner=None,
        control_outcome_learner=None,
        treatment_outcome_learner=None,
        treatment_effect_learner=None,
        ate_alpha=0.05,
        control_name=0,
    ):
        """Initialize a DR-learner.

        Args:
            learner (optional): a model to estimate outcomes and treatment effects in both the control and treatment
                groups
            control_outcome_learner (optional): a model to estimate outcomes in the control group
            treatment_outcome_learner (optional): a model to estimate outcomes in the treatment group
            treatment_effect_learner (optional): a model to estimate treatment effects in the treatment group
            ate_alpha (float, optional): the confidence level alpha of the ATE estimate
            control_name (str or int, optional): name of control group
        """
        assert (learner is not None) or (
            (control_outcome_learner is not None)
            and (treatment_outcome_learner is not None)
            and (treatment_effect_learner is not None)
        )

        if control_outcome_learner is None:
            self.model_mu_c = deepcopy(learner)
        else:
            self.model_mu_c = control_outcome_learner

        if treatment_outcome_learner is None:
            self.model_mu_t = deepcopy(learner)
        else:
            self.model_mu_t = treatment_outcome_learner

        if treatment_effect_learner is None:
            self.model_tau = deepcopy(learner)
        else:
            self.model_tau = treatment_effect_learner

        self.ate_alpha = ate_alpha
        self.control_name = control_name
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