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| 动态匹配估计量× | 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≠ | 2010 | 2000 |
| 提出者≠ | Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998) | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Nonparametric causal inference / matching | Causal model / semiparametric weighting |
| 开创性文献≠ | Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137. 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 | dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 相关≠ | 6 | 5 |
| 摘要≠ | The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions. | 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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