방법 비교

	기계 학습 증강 퍼지 회귀 불연속 설계	이중 강건 추정 (AIPW)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2001 (fuzzy RDD); 2018 (double ML augmentation)	2005
창시자	Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)	Robins & Rotnitzky; Bang & Robins
유형	Quasi-experimental causal inference	Semiparametric causal estimator
원전	Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Review of Economic Studies, 68(1), 201-209.	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.
별칭	ML-augmented fuzzy RDD, ML fuzzy RD, double ML fuzzy RDD, nonparametric fuzzy RDD	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
관련	5	5
요약	ML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. Where standard fuzzy RDD uses IV-style estimation at a threshold with imperfect compliance, the ML-augmented variant leverages nonparametric learners — such as random forests or neural networks — to model both the outcome and the first-stage treatment probability near the cutoff, reducing misspecification bias while preserving causal identification.	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.