Double score matching in observational studies with multi-level treatments

While weighting methods are popular for comparing the effects of multi-level treatment in observational studies, their performance can be unstable in the presence of extreme values of the generalized propensity score (GPS). Matching methods are more resistant to GPS outliers but bear the risk of GPS model misspecification. In this article, we propose a double score matching (DSM) estimator of the pairwise average treatment effect (ATE) based on the GPS and the generalized prognostic score (GPGS) evaluated at one treatment level at a time. The de-biased DSM estimator not only maintains the advantage of matching methods but also alleviates the model dependence problem due to its double robustness: it consistently estimates the true pairwise ATE if either the GPS or the GPGS is correctly specified.

Automated summary

This article proposes a double score matching (DSM) estimator for comparing the effects of multi-level treatment in observational studies. The DSM estimator not only maintains the advantages of matching methods, but also alleviates the model dependence problem through its double robustness.