Comparar métodos

	Análisis de sensibilidad espacial para la causalidad	Método de Variables Instrumentales (VI) para Inferencia Causal
Campo	Inferencia causal	Economía de la salud
Familia	Regression model	Process / pipeline
Año de origen	1988–2021 (developed progressively)	1990s (modern applications)
Autor original	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tipo	Sensitivity / robustness analysis	Method
Fuente seminal	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	IV, two-stage least squares, TSLS, causal estimation
Relacionados	6	3
Resumen	Spatial sensitivity analysis for causality systematically tests whether a causal estimate derived from georeferenced data holds up as spatial structure, spillovers, and the choice of spatial weights matrix are varied. Because nearby units often share unmeasured confounders — soil quality, local infrastructure, neighbourhood norms — a naive regression may yield biased causal estimates. This method reveals how fragile or robust a claimed causal effect is to alternative spatial specifications.	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.