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| 贝叶斯倾向得分加权× | 双重稳健估计（AIPW）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2009 | 2005 |
| 提出者≠ | McCandless, Gustafson & Austin | Robins & Rotnitzky; Bang & Robins |
| 类型≠ | Bayesian causal weighting estimator | Semiparametric causal estimator |
| 开创性文献≠ | McCandless, L. C., Gustafson, P., & Austin, P. C. (2009). Bayesian propensity score analysis for observational data. Statistics in Medicine, 28(1), 94–112. DOI ↗ | Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ |
| 别名 | Bayesian PSW, Bayesian IPW, Bayesian inverse probability weighting, Bayesian propensity weighting | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| 相关≠ | 6 | 5 |
| 摘要≠ | Bayesian Propensity Score Weighting estimates causal treatment effects in observational data by combining a Bayesian model for the propensity score with inverse probability weighting. By placing a prior over propensity-score parameters and propagating posterior uncertainty through the weighting step, this approach yields fully probabilistic uncertainty intervals for the average treatment effect, accounting for the uncertainty in both the score model and the outcome. | Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified. |
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