Comparar métodos

	Desenho de Regressão por Descontinuidade Difusa Espacial	Método de Variáveis Instrumentais (VI) para Inferência Causal
Área	Inferência causal	Economia da saúde
Família	Regression model	Process / pipeline
Ano de origem	2015	1990s (modern applications)
Autor original	Keele & Titiunik (2015); fuzzy extension of geographic RDD building on Imbens & Lemieux (2008)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Quasi-experimental causal inference / IV-based spatial design	Method
Fonte seminal	Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Outros nomes	Spatial Fuzzy RD, Geographic Fuzzy RDD, Spatial Fuzzy RDD, Geo-Fuzzy RD	IV, two-stage least squares, TSLS, causal estimation
Relacionados	5	3
Resumo	Spatial Fuzzy Regression Discontinuity Design (Spatial Fuzzy RDD) estimates a local average treatment effect when a geographic boundary determines treatment eligibility but some units on either side of the boundary fail to comply with their assigned status. It combines the spatial running-variable logic of geographic RDD with the instrumental-variable correction for imperfect compliance used in fuzzy RDD.	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.