So sánh phương pháp

	Thiết kế Phân tích Gián đoạn Mờ Tăng cường Học máy	Ước lượng Mạnh mẽ Kép (AIPW)
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2001 (fuzzy RDD); 2018 (double ML augmentation)	2005
Người khởi xướng	Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)	Robins & Rotnitzky; Bang & Robins
Loại	Quasi-experimental causal inference	Semiparametric causal estimator
Công trình gốc	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.
Tên gọi khác	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)
Liên quan	5	5
Tóm tắt	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.