Comparer des méthodes

	Conception d'étude d'événement pour l'évaluation des politiques	La régression par discontinuité (RDD)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1993-2021	2008
Auteur d'origine	Andrews (1993), MacKinlay (1997); formalized for policy evaluation by Freyaldenhoven, Hansen & Shapiro (2019) and Callaway & Sant'Anna (2021)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Type	Quasi-experimental / causal inference	Quasi-experimental causal design
Source fondatrice	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Alias	event study, event-study DiD, dynamic DiD, PEESD	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Apparentées	5	5
Résumé	A policy evaluation event study design is a quasi-experimental approach that estimates causal effects of a policy by plotting treatment-period-by-period coefficients around a common event time. It extends difference-in-differences to visualize both pre-treatment parallel trends and the dynamic post-treatment evolution of the policy effect, and has become the standard credibility check in applied policy research.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.