Comparer des méthodes

	Évaluation d'Impact Contrefactuel Spatial (EICS)	Régression Pondérée Géographiquement (GWR)
Domaine	Inférence causale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	2010s	2002
Auteur d'origine	Cerqua, Pellegrini, and regional-science scholars building on counterfactual econometrics	Fotheringham, Brunsdon & Charlton
Type	Quasi-experimental / causal inference	Local spatial regression
Source fondatrice	Cerqua, A., & Pellegrini, G. (2014). Do subsidies to private capital boost firms' growth? A multiple regression discontinuity design approach. Journal of Public Economics, 109, 114-126.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Alias	SCIE, spatial CIE, place-based counterfactual evaluation, regional counterfactual analysis	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Apparentées	5	5
Résumé	Spatial Counterfactual Impact Evaluation (SCIE) is a family of quasi-experimental methods that estimate the causal effect of geographically targeted policies — such as EU Cohesion Funds, enterprise zones, or place-based subsidies — by constructing a spatial counterfactual: what outcomes the treated region would have experienced without the intervention, inferred from comparable untreated regions or from discontinuities at policy boundaries.	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.