Comparer des méthodes

	Étude d'événement sur données de panel	Méthode des variables instrumentales (VI) pour l'inférence causale
Domaine	Inférence causale	Économie de la santé
Famille	Regression model	Process / pipeline
Année d'origine	2021	1990s (modern applications)
Auteur d'origine	Callaway & Sant'Anna (2021); Borusyak, Jaravel & Spiess (2024); Sun & Abraham (2021)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Causal inference / quasi-experimental panel design	Method
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: Princeton University Press.
Alias	panel event study, event-study DiD, staggered event study, difference-in-differences event study	IV, two-stage least squares, TSLS, causal estimation
Apparentées	6	3
Résumé	A panel event study is a quasi-experimental design that traces how an outcome evolves in periods before and after a policy event, using unit and time fixed effects to identify the causal effect. Widely used in economics and policy research, it tests for anticipation effects, verifies parallel pre-trends, and estimates dynamic treatment effects across post-treatment horizons — making it the standard toolkit for rigorous policy evaluation with observational panel data.	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.