Compara mètodes

	Anàlisi de sensibilitat espacial per a la causalitat	Regressió Ponderada Geogràficament (GWR)
Camp	Inferència causal	Anàlisi espacial
Família	Regression model	Regression model
Any d'origen	1988–2021 (developed progressively)	2002
Autor original	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Fotheringham, Brunsdon & Charlton
Tipus	Sensitivity / robustness analysis	Local spatial regression
Font seminal	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Àlies	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Relacionats	6	5
Resum	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.	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.