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| 面板数据倾向得分加权× | 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≠ | 2000-2003 | 2000 |
| 提出者≠ | Hirano, Imbens & Ridder; Robins, Hernan & Brumback | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Causal inference / panel weighting | Causal model / semiparametric weighting |
| 开创性文献≠ | Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189. 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 | panel PSW, panel IPW, longitudinal propensity score weighting, panel inverse probability weighting | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 相关 | 5 | 5 |
| 摘要≠ | Panel Data Propensity Score Weighting (panel PSW) extends inverse probability weighting to longitudinal settings where the same units are observed across multiple time periods. It reweights observations by the inverse of each unit's time-varying probability of receiving treatment, creating a pseudo-population in which treatment is balanced on observed covariates at each period, and then estimates causal effects from repeated-measures data. | 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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