主 题：Propensity score weighting in causal inference
Covariate balance is crucial for causal inference. However, lack of balance is common in observational studies. In this talk, we overview a general class of weighting strategies for balancing covariates: the balancing weights. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including inverse-probability weights as special cases. In particular, we focus on the overlap weighting (OW) scheme, in which each unit's weight is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded, minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights, and also possess a desirable small-sample exact balance property. The overlap weights target at the population in clinical equipoise, or more broadly, the population with substantial overlap in baseline characteristics between two groups. We discuss several scenarios in health data science where OW is particularly relevant, such as constructing external controls with real world data.