方法对比

	稳健倾向得分加权法	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	1994–2019	1983 (propensity score); 2003 (efficient IPW estimator)
提出者	Robins, Rotnitzky, & Zhao (foundational augmented IPW); Zhao, Small, & Bhattacharya (sensitivity-robust IPW)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
类型	Robust causal weighting estimator	Causal inference / reweighting
开创性文献	Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89(427), 846-866.	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 PSW, robust IPW, robustness-augmented propensity score weighting, misspecification-robust weighting	PSW, inverse probability weighting, IPW, propensity-based weighting
相关	6	6
摘要	Robust Propensity Score Weighting extends standard inverse probability weighting by incorporating safeguards against misspecification of the propensity score model and extreme weights. It combines techniques such as weight trimming, overlap weighting, or augmented outcome models to ensure that causal effect estimates remain reliable even when the propensity score model is imperfectly specified.	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).