手法を比較

	空間的因果関係感応度分析	地理的に重み付けされた回帰分析 (GWR)
分野	因果推論	空間分析
系統	Regression model	Regression model
提唱年	1988–2021 (developed progressively)	2002
提唱者	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Fotheringham, Brunsdon & Charlton
種類	Sensitivity / robustness analysis	Local spatial regression
原典	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
別名	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
関連	6	5
概要	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.