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| 贝叶斯边际结构模型× | Marginal Structural Model (MSM)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2015 (Bayesian extension); 2000 (MSM foundation) | 2000 |
| 提出者≠ | Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM) | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Causal inference / Bayesian weighted regression | Causal model / semiparametric weighting |
| 开创性文献≠ | 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 ↗ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 别名 | Bayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSM | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. | A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail. |
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