causalml/inference/iv/drivlearner.py [843:871]:
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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 DRIV-learner regressor.

        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
        """
        super().__init__(
            learner=learner,
            control_outcome_learner=control_outcome_learner,
            treatment_outcome_learner=treatment_outcome_learner,
            treatment_effect_learner=treatment_effect_learner,
            ate_alpha=ate_alpha,
            control_name=control_name,
        )
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causalml/inference/meta/drlearner.py [460:487]:
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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 an DR-learner regressor.

        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
        """
        super().__init__(
            learner=learner,
            control_outcome_learner=control_outcome_learner,
            treatment_outcome_learner=treatment_outcome_learner,
            treatment_effect_learner=treatment_effect_learner,
            ate_alpha=ate_alpha,
            control_name=control_name,
        )
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