Σύγκριση μεθόδων

	Στάθμιση Βαθμολογίας Χωρικής Προδιάθεσης	Παλινδρόμηση Γεωγραφικά Σταθμισμένη (GWR)
Πεδίο	Αιτιακή Συμπερασματολογία	Χωρική Ανάλυση
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2000s–2010s	2002
Δημιουργός	Extended from Hirano, Imbens & Ridder (2003) IPTW with spatial adaptations by Keele, Titiunik and others in geographically structured causal designs	Fotheringham, Brunsdon & Charlton
Τύπος	Quasi-experimental / causal inference	Local spatial regression
Θεμελιώδης πηγή	Keele, L., & Titiunik, R. (2015). Geographic Boundaries as Regression Discontinuities. Political Analysis, 23(1), 127-155.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Εναλλακτικές ονομασίες	spatial PSW, geographically weighted propensity score weighting, spatial IPTW, spatially adjusted inverse probability weighting	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Συναφείς	6	5
Σύνοψη	Spatial propensity score weighting extends inverse probability of treatment weighting (IPTW) to settings where units are geographically located and treatment assignment may depend on spatial factors such as location, neighborhood characteristics, or spatial clustering. By incorporating spatial covariates into the propensity score model and adjusting standard errors for spatial autocorrelation, it produces more credible causal estimates from observational geographic data.	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.