Comparer des méthodes

	Machine Learning-Augmented Fuzzy Regression Discontinuity	Régression par discontinuité floue
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2001 (fuzzy RDD); 2018 (double ML augmentation)	2001
Auteur d'origine	Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)	Hahn, Todd & van der Klaauw
Type	Quasi-experimental causal inference	Quasi-experimental causal inference
Source fondatrice	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.	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.
Alias	ML-augmented fuzzy RDD, ML fuzzy RD, double ML fuzzy RDD, nonparametric fuzzy RDD	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Apparentées	5	5
Résumé	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.	Fuzzy Regression Discontinuity Design (Fuzzy RDD) estimates causal effects when eligibility for a treatment is determined by a threshold on a running variable but actual take-up of that treatment is imperfect — some eligible units do not receive treatment and some ineligible units do. The cutoff acts as an instrument, and the estimand is a Local Average Treatment Effect (LATE) for compliers near the threshold.