Comparar métodos

	Análise de Sensibilidade Espacial para Causalidade	Diferenças em Diferenças (DiD)
Área	Inferência causal	Econometria
Família	Regression model	Regression model
Ano de origem	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
Fonte 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
Outros nomes	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Relacionados	6	5
Resumo	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.