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| 이질적 처리 효과의 이중 강건 추정× | 성향 점수 가중치 (PSW / IPW)× | |
|---|---|---|
| 분야 | 인과추론 | 인과추론 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2018-2023 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| 창시자≠ | Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| 유형≠ | Semiparametric causal inference | Causal inference / reweighting |
| 원전≠ | 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 ↗ |
| 별칭 | DR-HTE, augmented IPW for HTE, doubly robust CATE estimation, semiparametric HTE estimation | PSW, inverse probability weighting, IPW, propensity-based weighting |
| 관련≠ | 5 | 6 |
| 요약≠ | 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). |
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