InversePropensityWeighting.make_overlap_adjustments

InversePropensityWeighting.make_overlap_adjustments(ps)

Compute inverse-propensity-weighted outcomes using the overlap scheme.

This weighting scheme was adapted from Lucy D’Agostino McGowan’s blog on propensity score weights, referenced in the primary CausalPy explainer notebook. Overlap weights target the population for which there is clinical equipoise (i.e., where propensity scores are near 0.5), reducing sensitivity to extreme scores.

Parameters:

ps (ndarray) – Propensity scores for each observation.

Returns:

A tuple of (weighted_outcome0, weighted_outcome1, n_ntrt, n_trt) where the weighted outcomes and normalisation terms are all pd.Series (unlike the raw/robust schemes which return integer counts).

Return type:

tuple[pd.Series, pd.Series, pd.Series, pd.Series]