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| 策略评估双重稳健估计× | 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≠ | 1994-2005 | 2000 |
| 提出者≠ | Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005) | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Semiparametric causal estimator | Causal model / semiparametric weighting |
| 开创性文献≠ | Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973. 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 estimation for policy, augmented IPW for policy evaluation, AIPW policy evaluation, doubly robust policy analysis | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 相关 | 5 | 5 |
| 摘要≠ | Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation. | 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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