Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Байесово претегляне с обратна вероятност× | Байесовски метод за съпоставяне на склонността (Bayesian PSM)× | |
|---|---|---|
| Област | Причинно-следствено заключение | Причинно-следствено заключение |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2015 | 2012 |
| Създател≠ | Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020) | Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983) |
| Тип≠ | Bayesian causal weighting estimator | Bayesian causal inference / matching |
| Основополагащ източник≠ | 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. link ↗ | Kaplan, D., & Chen, J. (2012). A Two-Step Bayesian Approach for Propensity Score Analysis: Simulations and Case Study. Psychometrika, 77(3), 581-609. DOI ↗ |
| Други названия | Bayesian IPW, BIPW, Bayesian propensity-weighted estimation, Bayesian marginal structural weighting | Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting |
| Свързани | 6 | 6 |
| Резюме≠ | 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. | 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. |
| ScholarGateНабор от данни ↗ |
|
|