方法对比
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| 异质性处理效应逆概率加权法 (HTE-IPW)× | 逆概率治疗加权法 (IPW / IPTW)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2003–2015 | 2000 |
| 提出者≠ | Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli | Robins, Hernán & Brumback |
| 类型≠ | Causal inference / weighted regression | Causal inference weighting estimator |
| 开创性文献≠ | Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| 别名≠ | HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| 相关 | 5 | 5 |
| 摘要≠ | HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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