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。
| 多期反事实概率加权法× | 面板数据逆概率加权× | |
|---|---|---|
| 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 | Robins, Hernan & Brumback |
| 类型≠ | Weighted causal estimator | Reweighting / causal inference |
| 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 | longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW | panel IPW, longitudinal IPW, time-varying IPW, panel IPTW |
| 相关≠ | 6 | 5 |
| 摘要≠ | Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies. | 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. |
| ScholarGate数据集 ↗ |
|
|