causalml/metrics/visualize.py [113:156]:
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):
    """Get average uplifts of model estimates in cumulative population.

    If the true treatment effect is provided (e.g. in synthetic data), it's calculated
    as the mean of the true treatment effect in each of cumulative population.
    Otherwise, it's calculated as the difference between the mean outcomes of the
    treatment and control groups in each of cumulative population.

    For details, see Section 4.1 of Gutierrez and G{\'e}rardy (2016), `Causal Inference
    and Uplift Modeling: A review of the literature`.

    For the former, `treatment_effect_col` should be provided. For the latter, both
    `outcome_col` and `treatment_col` should be provided.

    Args:
        df (pandas.DataFrame): a data frame with model estimates and actual data as columns
        outcome_col (str, optional): the column name for the actual outcome
        treatment_col (str, optional): the column name for the treatment indicator (0 or 1)
        treatment_effect_col (str, optional): the column name for the true treatment effect
        random_seed (int, optional): deprecated

    Returns:
        (pandas.DataFrame): average uplifts of model estimates in cumulative population
    """
    assert (
        (outcome_col in df.columns and df[outcome_col].notnull().all())
        and (treatment_col in df.columns and df[treatment_col].notnull().all())
        or (
            treatment_effect_col in df.columns
            and df[treatment_effect_col].notnull().all()
        )
    ), "{outcome_col} and {treatment_col}, or {treatment_effect_col} should be present without null.".format(
        outcome_col=outcome_col,
        treatment_col=treatment_col,
        treatment_effect_col=treatment_effect_col,
    )

    df = df.copy()

    model_names = [
        x
        for x in df.columns
        if x not in [outcome_col, treatment_col, treatment_effect_col]
    ]
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causalml/metrics/visualize.py [244:288]:
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):
    """Get Qini of model estimates in population.

    If the true treatment effect is provided (e.g. in synthetic data), it's calculated
    as the cumulative gain of the true treatment effect in each population.
    Otherwise, it's calculated as the cumulative difference between the mean outcomes
    of the treatment and control groups in each population.

    For details, see Radcliffe (2007), `Using Control Group to Target on Predicted Lift:
    Building and Assessing Uplift Models`

    For the former, `treatment_effect_col` should be provided. For the latter, both
    `outcome_col` and `treatment_col` should be provided.

    Args:
        df (pandas.DataFrame): a data frame with model estimates and actual data as columns
        outcome_col (str, optional): the column name for the actual outcome
        treatment_col (str, optional): the column name for the treatment indicator (0 or 1)
        treatment_effect_col (str, optional): the column name for the true treatment effect
        normalize (bool, optional): whether to normalize the y-axis to 1 or not
        random_seed (int, optional): deprecated

    Returns:
        (pandas.DataFrame): cumulative gains of model estimates in population
    """
    assert (
        (outcome_col in df.columns and df[outcome_col].notnull().all())
        and (treatment_col in df.columns and df[treatment_col].notnull().all())
        or (
            treatment_effect_col in df.columns
            and df[treatment_effect_col].notnull().all()
        )
    ), "{outcome_col} and {treatment_col}, or {treatment_effect_col} should be present without null.".format(
        outcome_col=outcome_col,
        treatment_col=treatment_col,
        treatment_effect_col=treatment_effect_col,
    )

    df = df.copy()

    model_names = [
        x
        for x in df.columns
        if x not in [outcome_col, treatment_col, treatment_effect_col]
    ]
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