Comparer des méthodes

	Évaluation de politiques par devis-régression (Regression Discontinuity Design)	Régression par discontinuité floue
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1960; policy evaluation applications widespread from 2000s	2001
Auteur d'origine	Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)	Hahn, Todd & van der Klaauw
Type	Quasi-experimental causal design	Quasi-experimental causal inference
Source fondatrice	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.	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	Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact	Fuzzy RD, Fuzzy RDD, Fuzzy RD Design, Imperfect RDD
Apparentées	5	5
Résumé	Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves.	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.