InversePropensityWeighting.make_doubly_robust_adjustment

InversePropensityWeighting.make_doubly_robust_adjustment(ps)

Compute doubly-robust adjusted outcomes.

The doubly-robust weighting scheme is discussed in Aronow and Miller’s Foundations of Agnostic Statistics. This implementation fixes the outcome model to ordinary least squares (OLS), so the compromise between the outcome model and the propensity model is always performed with a linear regression.

Parameters:

ps (ndarray) – Propensity scores for each observation.

Returns:

A tuple of (weighted_outcome0, weighted_outcome1, None, None). The two None values are returned for interface consistency with the other adjustment methods; no explicit group sizes are needed because the doubly-robust estimator averages over all observations.

Return type:

tuple[pd.Series, pd.Series, None, None]