Score

Abstract: The propensity score plays a central role in a variety of causal inference settings. In particular, matching and weighting methods based on the estimated propensity score have become increasingly common in observational studies. Despite their popularity and theoretical appeal, the main practical difficulty of these methods is that the propensity score must be estimated. Researchers have found that slight misspecification of the propensity score model can result in substantial bias of estimated treatment effects. This workshop introduces a simple and yet powerful new methodology, covariate balancing propensity score (CBPS) estimation, which significantly improves the empirical performance of propensity score methods. The CBPS simultaneously optimizes the covariate balance and the prediction of treatment assignment by exploiting the dual characteristics of the propensity score as a covariate balancing score and the conditional probability of treatment assignment. The CBPS is shown to dramatically improve the poor empirical performance of propensity score matching and weighting methods reported in the literature. In addition, the CBPS can be extended to a number of other important settings, including the estimation of the generalized propensity score for non-binary treatments, the generalization of experimental estimates to a target population, and causal inference in the longitudinal settings with marginal structural models. The open-source R package, CBPS, is available for implementing the proposed methods.