New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators
扩展了Frölich（2004）的研究，通过实证和假设的数据生成过程分析，发现重新加权估计量在重叠良好时与最有效的匹配估计量相当，但在重叠较差时匹配更有效。
Fr�lich (2004) compares the finite sample properties of reweighting and matching estimators of average treatment effects and concludes that reweighting performs far worse than even the simplest matching estimator. We argue that this conclusion is unjustified. Neither approach dominates the other uniformly across data-generating processes (DGPs). Expanding on Fr�lich's analysis, this paper analyzes empirical as well as hypothetical DGPs and also examines the effect of misspecification. We conclude that reweighting is competitive with the most effective matching estimators when overlap is good, but that matching may be more effective when overlap is sufficiently poor.