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| 異質的処置効果傾向スコアマッチング× | 二重に頑健な推定量（AIPW）× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1983–2016 | 2005 |
| 提唱者≠ | Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016) | Robins & Rotnitzky; Bang & Robins |
| 種類≠ | Causal inference / matching with effect heterogeneity | Semiparametric causal estimator |
| 原典≠ | Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360. DOI ↗ | 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 ↗ |
| 別名 | HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) |
| 関連 | 5 | 5 |
| 概要≠ | Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment. | 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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