Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| Pesha e Probabilitetit të Inversit Bayesiane× | Përputhja me Rezultatë të Prapavijës Bayesian (Bayesian PSM)× | |
|---|---|---|
| Fusha | Inferenca kauzale | Inferenca kauzale |
| Familja | Regression model | Regression model |
| Viti i origjinës≠ | 2015 | 2012 |
| Krijuesi≠ | Saarela, Stephens, Moodie & Klein (2015); Liao & Zigler (2020) | Kaplan & Chen (2012); foundational PSM by Rosenbaum & Rubin (1983) |
| Lloji≠ | Bayesian causal weighting estimator | Bayesian causal inference / matching |
| Burimi themelues≠ | 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 ↗ |
| Emërtime të tjera | Bayesian IPW, BIPW, Bayesian propensity-weighted estimation, Bayesian marginal structural weighting | Bayesian PSM, BPSM, Bayesian matching estimator, Bayesian propensity weighting |
| Të lidhura | 6 | 6 |
| Përmbledhja≠ | 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. |
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