Comparer des méthodes

	Conception d'étude d'événement pour l'évaluation des politiques	Différence-en-différences (Diff-in-Diff)
Domaine	Inférence causale	Économétrie
Famille	Regression model	Regression model
Année d'origine	1993-2021	1994
Auteur d'origine	Andrews (1993), MacKinlay (1997); formalized for policy evaluation by Freyaldenhoven, Hansen & Shapiro (2019) and Callaway & Sant'Anna (2021)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Quasi-experimental / causal inference	Causal inference / panel regression
Source fondatrice	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	event study, event-study DiD, dynamic DiD, PEESD	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.