Порівняння методів

	Машинне навчання-доповнений нечіткий розрив регресії	Подвійне робастне оцінювання (AIPW)
Галузь	Причинно-наслідковий висновок	Причинно-наслідковий висновок
Родина	Regression model	Regression model
Рік появи	2001 (fuzzy RDD); 2018 (double ML augmentation)	2005
Автор методу	Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)	Robins & Rotnitzky; Bang & Robins
Тип	Quasi-experimental causal inference	Semiparametric causal estimator
Основоположне джерело	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.
Інші назви	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)
Пов'язані	5	5
Підсумок	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.