Comparar métodos

	Método de Variables Instrumentales (VI) para Inferencia Causal	Modelo de Efectos Fijos para Datos de Panel
Campo	Economía de la salud	Econometría
Familia	Process / pipeline	Regression model
Año de origen	1990s (modern applications)	2014
Autor original	Angrist & Pischke (applied econometrics); rooted in econometric theory	Hsiao (textbook treatment); within transformation of panel data
Tipo	Method	Panel data regression
Fuente seminal	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Alias	IV, two-stage least squares, TSLS, causal estimation	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Relacionados	3	5
Resumen	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.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).