方法对比
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| 稳健边际结构模型× | 面板数据边际结构模型 (MSM)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2000–2004 | 2000 |
| 提出者≠ | Robins, Hernán & Brumback; robustness extensions by Scharfstein, Rotnitzky, Lunceford & Davidian | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Causal inference / weighted regression | Causal model for time-varying treatments |
| 开创性文献≠ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. 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 | robust MSM, doubly-robust MSM, sandwich-SE MSM, robust IPTW marginal structural model | MSM panel, longitudinal MSM, panel MSM, time-varying treatment MSM |
| 相关≠ | 6 | 5 |
| 摘要≠ | Robust Marginal Structural Models (robust MSMs) extend the standard MSM framework — which uses inverse probability of treatment weighting to handle time-varying confounding — by pairing IPTW estimation with sandwich (robust) standard errors or doubly-robust estimators. This combination yields valid causal estimates and reliable inference even when the outcome regression model is mildly misspecified or weights are moderately variable. | A panel data marginal structural model (MSM) uses inverse probability of treatment weighting (IPTW) across multiple time periods to estimate the causal effect of a time-varying treatment, while appropriately adjusting for time-varying confounders that are themselves affected by prior treatment — a bias source that conventional regression cannot handle. |
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