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。
| 面板数据逆概率加权× | 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 | 2000 | 2000 |
| 提出者≠ | Robins, Hernan & Brumback | James M. Robins, Miguel A. Hernan, Babette Brumback |
| 类型≠ | Reweighting / causal inference | Causal model / semiparametric weighting |
| 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 ↗ | 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 | panel IPW, longitudinal IPW, time-varying IPW, panel IPTW | MSM, MSM-IPTW, marginal structural Cox model, weighted structural model |
| 相关 | 5 | 5 |
| 摘要≠ | Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods. | 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. |
| ScholarGate数据集 ↗ |
|
|