Comparar métodos

	Análisis de sensibilidad espacial para la causalidad	Diferencia en Diferencias (Diff-in-Diff)
Campo	Inferencia causal	Econometría
Familia	Regression model	Regression model
Año de origen	1988–2021 (developed progressively)	1994
Autor original	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Sensitivity / robustness analysis	Causal inference / panel regression
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 University Press. ISBN: 978-0691120355
Alias	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	6	5
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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.