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| 动态倾向得分匹配× | 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≠ | 1986-2010 | 2000 |
| 提出者≠ | Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Sequential causal 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 PSM, sequential propensity score matching, longitudinal propensity matching, DPSM | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 相关≠ | 6 | 5 |
| 摘要≠ | Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments. | 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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