方法对比

	稳健倾向得分匹配	倾向得分加权法 (PSW / IPW)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2016 (robust variance correction); 1983 (PSM foundations)	1983 (propensity score); 2003 (efficient IPW estimator)
提出者	Abadie & Imbens (2016) for matching-on-estimated-propensity-score with corrected variance; Rosenbaum & Rubin (1983) for PSM foundations	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
类型	Quasi-experimental matching estimator with robust inference	Causal inference / reweighting
开创性文献	Abadie, A., & Imbens, G. W. (2016). Matching on the Estimated Propensity Score. Econometrica, 84(2), 781-807.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
别名	robust PSM, PSM with robust variance, bias-corrected PSM, matching with robust inference	PSW, inverse probability weighting, IPW, propensity-based weighting
相关	6	6
摘要	Robust Propensity Score Matching (robust PSM) is a quasi-experimental causal inference method that pairs treated and control units on their estimated probability of receiving treatment (the propensity score), then estimates the average treatment effect using variance estimators that account for the uncertainty introduced by estimating the propensity score itself. The correction, developed by Abadie and Imbens (2016), prevents misleading inference that standard bootstrap or analytic formulas produce when applied naively after matching.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).