方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| Marginal Structural Model (MSM)× | 倾向得分加权法 (PSW / IPW)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2000 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| 提出者≠ | James M. Robins, Miguel A. Hernan, Babette Brumback | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| 类型≠ | Causal model / semiparametric weighting | Causal inference / reweighting |
| 开创性文献≠ | Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗ |
| 别名 | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model | PSW, inverse probability weighting, IPW, propensity-based weighting |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). |
| ScholarGate数据集 ↗ |
|
|