causalml/dataset/regression.py [87:102]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        sigma (float): standard deviation of the error term
        adj (float): no effect. added for consistency


    Returns:
        (tuple): Synthetically generated samples with the following outputs:

            - y ((n,)-array): outcome variable.
            - X ((n,p)-ndarray): independent variables.
            - w ((n,)-array): treatment flag with value 0 or 1.
            - tau ((n,)-array): individual treatment effect.
            - b ((n,)-array): expected outcome.
            - e ((n,)-array): propensity of receiving treatment.
    '''

    X = np.random.normal(size=n*p).reshape((n, -1))
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causalml/dataset/regression.py [120:134]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        sigma (float): standard deviation of the error term
        adj (float): no effect. added for consistency

    Returns:
        (tuple): Synthetically generated samples with the following outputs:

            - y ((n,)-array): outcome variable.
            - X ((n,p)-ndarray): independent variables.
            - w ((n,)-array): treatment flag with value 0 or 1.
            - tau ((n,)-array): individual treatment effect.
            - b ((n,)-array): expected outcome.
            - e ((n,)-array): propensity of receiving treatment.
    '''

    X = np.random.normal(size=n*p).reshape((n, -1))
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