विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| बायेसियन मार्जिनल स्ट्रक्चरल मॉडल (Bayesian Marginal Structural Model)× | प्रोपेंसिटी स्कोर वेटिंग (PSW / IPW)× | |
|---|---|---|
| क्षेत्र | कारणात्मक अनुमान | कारणात्मक अनुमान |
| परिवार | Regression model | Regression model |
| उद्भव वर्ष≠ | 2015 (Bayesian extension); 2000 (MSM foundation) | 1983 (propensity score); 2003 (efficient IPW estimator) |
| प्रवर्तक≠ | Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| प्रकार≠ | Causal inference / Bayesian weighted regression | Causal inference / reweighting |
| मौलिक स्रोत≠ | Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288. DOI ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗ |
| उपनाम | Bayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSM | PSW, inverse probability weighting, IPW, propensity-based weighting |
| संबंधित | 6 | 6 |
| सारांश≠ | Bayesian Marginal Structural Model (Bayesian MSM) combines the causal identification power of inverse-probability-weighted marginal structural models with Bayesian posterior inference. Rather than relying on point estimates and asymptotic standard errors, it propagates uncertainty through a full posterior distribution over causal effect parameters, offering coherent uncertainty quantification for causal effects of time-varying treatments. | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). |
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