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| 异质性处理效应双重稳健估计× | 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≠ | 2018-2023 | 2000 |
| 提出者≠ | Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018) | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Semiparametric causal inference | Causal model / semiparametric weighting |
| 开创性文献≠ | Kennedy, E. H. (2023). Towards optimal doubly robust estimation of heterogeneous causal effects. Electronic Journal of Statistics, 17(2), 3008-3049. 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 | DR-HTE, augmented IPW for HTE, doubly robust CATE estimation, semiparametric HTE estimation | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 相关 | 5 | 5 |
| 摘要≠ | Doubly robust estimation of heterogeneous treatment effects (HTE) estimates how the causal effect of a treatment varies across subgroups or individual covariate values. By combining an outcome model and a propensity score model, it retains consistency if either model is correctly specified, and supports flexible machine learning nuisance estimators through cross-fitting to produce valid conditional average treatment effect (CATE) estimates. | 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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