方法对比
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| 面板数据边际结构模型 (MSM)× | 面板数据逆概率加权× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份 | 2000 | 2000 |
| 提出者≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Robins, Hernan & Brumback |
| 类型≠ | Causal model for time-varying treatments | Reweighting / causal inference |
| 开创性文献 | Robins, J. M., Hernan, 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 | MSM panel, longitudinal MSM, panel MSM, time-varying treatment MSM | panel IPW, longitudinal IPW, time-varying IPW, panel IPTW |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods. |
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