方法对比
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| 面板数据边际结构模型 (MSM)× | 逆概率治疗加权法 (IPW / IPTW)× | |
|---|---|---|
| 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, Hernán & Brumback |
| 类型≠ | Causal model for time-varying treatments | Causal inference weighting estimator |
| 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., Hernán, 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 | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 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. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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