Comparer des méthodes

	La régression floue par discontinuité (Fuzzy RDD) en recherche sur l'éducation	Méthode des variables instrumentales (VI) pour l'inférence causale
Domaine	Inférence causale	Économie de la santé
Famille	Regression model	Process / pipeline
Année d'origine	Late 1990s–2000s	1990s (modern applications)
Auteur d'origine	Imbens & Lemieux (2008); applied in education by Jacob & Lefgren (2004) and Angrist & Lavy (1999)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Quasi-experimental / causal inference	Method
Source fondatrice	Imbens, G. W., & Lemieux, T. (2008). Regression discontinuity designs: A guide to practice. Journal of Econometrics, 142(2), 615-635.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	Fuzzy RDD, Fuzzy RD, Imperfect RDD, Non-sharp RD	IV, two-stage least squares, TSLS, causal estimation
Apparentées	4	3
Résumé	Fuzzy Regression Discontinuity Design (Fuzzy RDD) is a quasi-experimental causal method that exploits a known score threshold — such as a test cutoff — to estimate the effect of a program or intervention when assignment is imperfect. Widely used in education research to evaluate summer school, remedial programs, scholarships, and class-size rules, it uses two-stage least squares to recover a local average treatment effect for students near the threshold.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.