Comparer des méthodes

	Régression par discontinuité floue	Effet Traitement Moyen Local (ETML / CACE)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2001	1994
Auteur d'origine	Hahn, Todd & van der Klaauw	Imbens & Angrist (1994); Angrist, Imbens & Rubin (1996)
Type	Quasi-experimental causal inference	Instrumental-variable causal estimand
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.	Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.
Alias	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD	LATE, CACE, complier average causal effect, Yerel Ortalama Tedavi Etkisi (LATE / CACE)
Apparentées	5	5
Résumé	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.	The Local Average Treatment Effect is an instrumental-variable estimand, introduced by Imbens and Angrist (1994) and formalised with Rubin (1996), that recovers the average treatment effect for the subpopulation of compliers — units whose treatment status is actually moved by the instrument. It is closely tied to compliance analysis.