Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesovské párování skóre sklonu× | Dvojitě robustní odhad (AIPW)× | |
|---|---|---|
| Obor | Kauzální inference | Kauzální inference |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2012 | 2005 |
| Tvůrce≠ | Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983) | Robins & Rotnitzky; Bang & Robins |
| Typ≠ | Bayesian causal inference / matching | Semiparametric causal estimator |
| Původní zdroj≠ | Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609. 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 ↗ |
| Další názvy | Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| Příbuzné≠ | 6 | 5 |
| Shrnutí≠ | Bayesian Propensity Score Matching (Bayesian PSM) extends classical propensity score matching by placing a prior distribution over the propensity model parameters and propagating posterior uncertainty through the matching and outcome stages. Introduced formally by Kaplan and Chen (2012), it offers a principled account of estimation uncertainty that frequentist matching commonly ignores, and allows incorporation of substantive prior knowledge about treatment selection. | 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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