How to use the causallib.estimation.base_weight.PropensityEstimator function in causallib

def __init__(self, estimator):
        """
        Args:
            estimator (PropensityEstimator):
        """
        if not isinstance(estimator, PropensityEstimator):
            raise TypeError("PropensityEvaluator should be initialized with PropensityEstimator, got ({}) instead."
                            .format(type(estimator)))
        super(PropensityEvaluator, self).__init__(estimator)

"""

import warnings

import pandas as pd

from .base_estimator import PopulationOutcomeEstimator
from .base_weight import PropensityEstimator
from ..utils.stat_utils import robust_lookup


# TODO: implement a two-caliper truncation, one lower bound truncation epsilon and an upper bound one.


class IPW(PropensityEstimator, PopulationOutcomeEstimator):
    """
    Causal model implementing inverse probability (propensity score) weighting.
    w_i = 1 / Pr[A=a_i|Xi]
    """

    def __init__(self, learner, truncate_eps=None, use_stabilized=False):
        """

        Args:
            learner: Initialized sklearn model.
            truncate_eps (None|float): Optional value between 0 to 0.5 to clip the propensity estimation.
                                       Will clip probabilities between clip_eps and 1-clip_eps.
            use_stabilized (bool): Whether to re-weigh the learned weights with the prevalence of the treatment.
                                   See Also: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351790/#S6title
        """
        super(IPW, self).__init__(learner, use_stabilized)

def __init__(self, learner, use_stabilized=False, *args, **kwargs):
        """

        Args:
            learner: Initialized sklearn model.
            use_stabilized (bool): Whether to re-weigh the learned weights with the prevalence of the treatment.
                                   See Also: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351790/#S6title
        """
        super(PropensityEstimator, self).__init__(learner, use_stabilized=use_stabilized)
        if not hasattr(self.learner, "predict_proba"):
            raise AttributeError("Propensity Estimator must use a machine learning that can predict probabilities"
                                 "(i.e., have predict_proba method)")