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Abstract:
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Weighting and subclassification are popular approaches using propensity scores (PSs) for estimation of causal effects. Weighting is appealing in that it gives consistent estimators for various causal estimands if appropriate weights are well defined and the PS model is correctly specified. Subclassification is known to be more robust to model misspecification than weighting, but its application to diverse causal estimands is limited. In this article, we propose generalized stratum weights to implement subclassification estimators for various causal estimands. For this, we incorporate strata into the expression of the weighted average treatment effect (WATE). Particularly, we identify stratum weights for the ATE for the overlap population (ATO). We show that the identified stratum weights for ATO are equivalent to the optimal stratum weights, which are the inverse variances of the stratum-specific estimators. Simulation studies demonstrate that the proposed subclassification estimator for ATO is more robust to model misspecification than the weighting estimator for ATO. The practical utility of the proposed methods is illustrated in a study of right heart catheterization.
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