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| Bayesian Propensity Score Matching× | Ważenie odwrotnością prawdopodobieństwa leczenia (IPW / IPTW)× | |
|---|---|---|
| Dziedzina | Wnioskowanie przyczynowe | Wnioskowanie przyczynowe |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2012 | 2000 |
| Twórca≠ | Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983) | Robins, Hernán & Brumback |
| Typ≠ | Bayesian causal inference / matching | Causal inference weighting estimator |
| Źródło pierwotne≠ | 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., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Inne nazwy≠ | Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | 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. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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