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。
| 异质性处理效应双重稳健估计× | 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≠ | 2018-2023 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| 提出者≠ | Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| 类型≠ | Semiparametric causal inference | Causal inference / reweighting |
| 开创性文献≠ | Kennedy, E. H. (2023). Towards optimal doubly robust estimation of heterogeneous causal effects. Electronic Journal of Statistics, 17(2), 3008-3049. 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 | DR-HTE, augmented IPW for HTE, doubly robust CATE estimation, semiparametric HTE estimation | PSW, inverse probability weighting, IPW, propensity-based weighting |
| 相关≠ | 5 | 6 |
| 摘要≠ | Doubly robust estimation of heterogeneous treatment effects (HTE) estimates how the causal effect of a treatment varies across subgroups or individual covariate values. By combining an outcome model and a propensity score model, it retains consistency if either model is correctly specified, and supports flexible machine learning nuisance estimators through cross-fitting to produce valid conditional average treatment effect (CATE) estimates. | 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 ↗ |
|
|