InversePropensityWeighting.make_robust_adjustments

InversePropensityWeighting.make_robust_adjustments(ps)

Compute inverse-propensity-weighted outcomes using the robust (Horvitz-Thompson) scheme.

This estimator is discussed in Aronow and Miller’s Foundations of Agnostic Statistics as being related to the Horvitz-Thompson method.

Parameters:

ps (ndarray) – Propensity scores for each observation.

Returns:

A tuple of (weighted_outcome0, weighted_outcome1, n_ntrt, n_trt) where the weighted outcomes are the IPW-adjusted outcome values for the control and treated groups, and n_ntrt / n_trt are the corresponding group sizes used for normalisation.

Return type:

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