Compara mètodes

	Disseny d'estudi d'esdeveniments en investigació educativa	Mètode de Variables Instrumentals (IV) per a la Inferència Causal
Camp	Inferència causal	Economia de la salut
Família	Regression model	Process / pipeline
Any d'origen	1993 (general); 2000s–2010s (education applications)	1990s (modern applications)
Autor original	Jacobson, LaLonde & Sullivan (1993); popularized in education by Lafortune, Rothstein & Schanzenbach (2018) and subsequent education-policy literature	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipus	Quasi-experimental / causal inference	Method
Font seminal	Jacobson, L. S., LaLonde, R. J., & Sullivan, D. G. (1993). Earnings Losses of Displaced Workers. American Economic Review, 83(4), 685-709.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Àlies	event study, education event study, policy event study, dynamic difference-in-differences	IV, two-stage least squares, TSLS, causal estimation
Relacionats	5	3
Resum	An event study design tracks how educational outcomes evolve before and after a clearly defined event — such as a school finance reform, accountability policy, or curriculum change — for affected and unaffected units. By estimating period-by-period treatment effects relative to a baseline period, it delivers both a causal estimate of the policy's impact and a transparent test of the parallel-trends assumption underpinning difference-in-differences.	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.