Comparar métodos

	Diseño de Discontinuidad por Regresión Difusa con Datos de Panel	Método de Variables Instrumentales (VI) para Inferencia Causal
Campo	Inferencia causal	Economía de la salud
Familia	Regression model	Process / pipeline
Año de origen	2001 (fuzzy RDD); panel extension circa 2011	1990s (modern applications)
Autor original	Hahn, Todd & Van der Klaauw; extended to panel settings by Papay, Willett & Murnane and others	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Quasi-experimental causal inference	Method
Fuente seminal	Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	Panel Fuzzy RDD, Panel FRD, Fuzzy RD with Panel Data, Panel Fuzzy RD	IV, two-stage least squares, TSLS, causal estimation
Relacionados	5	3
Resumen	Panel Data Fuzzy Regression Discontinuity Design (Panel FRD) extends the fuzzy RDD framework to settings where multiple observations per unit are available over time. It exploits a probabilistic — rather than deterministic — threshold-crossing rule to identify a local average treatment effect (LATE) while controlling for unit-level and time-level fixed effects, sharpening identification in repeated-measures contexts.	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.