方法对比

	贝叶斯逆概率加权法	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	2015	1983 (propensity score); 2003 (efficient IPW estimator)
提出者	Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
类型	Bayesian causal weighting estimator	Causal inference / reweighting
开创性文献	Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On risk prediction and characterisation of treatment effects in a Bayesian framework using the propensity score. Statistics in Medicine, 34(14), 2170-2185.	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	Bayesian IPW, BIPW, Bayesian propensity-weighted estimation, Bayesian marginal structural weighting	PSW, inverse probability weighting, IPW, propensity-based weighting
相关	6	6
摘要	Bayesian Inverse Probability Weighting (Bayesian IPW) extends the classical IPW estimator by placing prior distributions over the propensity-score model parameters and propagating that uncertainty into the causal-effect estimate. The result is a posterior distribution for the average treatment effect that fully accounts for both propensity-score estimation uncertainty and outcome-model uncertainty, enabling credible-interval inference rather than relying on asymptotic approximations.	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).