Comparar métodos

	Desenho Robusto de Regressão Fuzzy por Descontinuidade	Método de Variáveis Instrumentais (VI) para Inferência Causal
Área	Inferência causal	Economia da saúde
Família	Regression model	Process / pipeline
Ano de origem	2014 (robust CCT estimator); 2001 (fuzzy RDD formalization)	1990s (modern applications)
Autor original	Calonico, Cattaneo, and Titiunik (robust inference framework); Hahn, Todd, and Van der Klaauw (fuzzy RDD formalization)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Quasi-experimental causal inference with IV at threshold	Method
Fonte seminal	Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica, 82(6), 2295-2326.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Outros nomes	Robust Fuzzy RDD, Fuzzy RD with robust inference, bias-corrected fuzzy RD, CCT fuzzy RDD	IV, two-stage least squares, TSLS, causal estimation
Relacionados	5	3
Resumo	Robust Fuzzy Regression Discontinuity Design estimates a local average treatment effect (LATE) at a threshold where crossing the cutoff raises — but does not guarantee — treatment receipt. Introduced by Calonico, Cattaneo, and Titiunik (2014), the robust framework applies bias-corrected local polynomial estimation with a robust variance estimator, correcting the coverage failures of conventional bandwidth-optimal inference in both the sharp and fuzzy cases.	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.