Comparer des méthodes

	Analyse de sensibilité spatiale pour la causalité	Régression Pondérée Géographiquement (GWR)
Domaine	Inférence causale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1988–2021 (developed progressively)	2002
Auteur d'origine	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Fotheringham, Brunsdon & Charlton
Type	Sensitivity / robustness analysis	Local spatial regression
Source fondatrice	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
Alias	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Apparentées	6	5
Résumé	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.