Comparar métodos

	Variables Instrumentales de Efecto de Tratamiento Heterogéneo (HTE-IV)	Método de Variables Instrumentales (VI) para Inferencia Causal
Campo	Inferencia causal	Economía de la salud
Familia	Regression model	Process / pipeline
Año de origen	1994	1990s (modern applications)
Autor original	Imbens & Angrist	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Causal inference / IV with effect heterogeneity	Method
Fuente seminal	Imbens, G. W., & Angrist, J. D. (1994). Identification and Estimation of Local Average Treatment Effects. Econometrica, 62(2), 467-475.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	HTE-IV, LATE estimator, IV with effect heterogeneity, local average treatment effect IV	IV, two-stage least squares, TSLS, causal estimation
Relacionados	4	3
Resumen	Heterogeneous treatment effect IV applies instrumental variables estimation while explicitly acknowledging and modelling that the treatment effect differs across units. Rather than recovering a single average effect, it focuses on the Local Average Treatment Effect (LATE) — the causal effect for compliers, the subpopulation whose treatment status is actually shifted by the instrument — and extends analysis to variation in that effect across observed subgroups.	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.