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| 异质性处理效应逆概率加权法 (HTE-IPW)× | 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≠ | 2003–2015 | 2000 |
| 提出者≠ | Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Causal inference / weighted regression | 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 | HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 相关 | 5 | 5 |
| 摘要≠ | HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics. | 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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