Comparar métodos

	Desenho Robusto de Regressão 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	1990s (modern applications)
Autor original	Calonico, Cattaneo & Titiunik	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Quasi-experimental causal inference	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 RDD, Bias-corrected RDD, CCT estimator, rdrobust	IV, two-stage least squares, TSLS, causal estimation
Relacionados	4	3
Resumo	Robust RDD extends the classical regression discontinuity design with bias correction and robust confidence intervals, addressing the under-coverage problem of conventional RDD inference. Developed by Calonico, Cattaneo, and Titiunik (2014), it uses local polynomial estimation with a bias-corrected point estimate and a wider variance term that accounts for the added uncertainty, yielding confidence intervals with correct asymptotic coverage.	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.