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| 异质性处理效应敏感性分析× | 双重稳健估计（AIPW）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2000s–2010s | 2005 |
| 提出者≠ | Rosenbaum (sensitivity analysis framework); extended to heterogeneous effects by Crump, Imbens, and others | Robins & Rotnitzky; Bang & Robins |
| 类型≠ | Robustness / sensitivity check | Semiparametric causal estimator |
| 开创性文献≠ | Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679 | Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ |
| 别名 | HTE sensitivity analysis, heterogeneous-effects sensitivity analysis, sensitivity analysis with effect heterogeneity, HTE robustness analysis | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| 相关 | 5 | 5 |
| 摘要≠ | Heterogeneous Treatment Effect Sensitivity Analysis examines how robust subgroup-specific causal estimates are to unobserved confounding. Rather than testing a single average treatment effect, it asks whether the estimated variation in treatment effects across units or subgroups could be explained away by hidden bias, and at what level of hidden bias the causal conclusions for each subgroup would break down. | Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified. |
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