Módszerek összehasonlítása

	Térbeli Inverz Valószínűségi Súlyozás (Spatial IPW)	Földrajzilag súlyozott regresszió (GWR)
Tudományterület	Oksági következtetés	Térbeli elemzés
Módszercsalád	Regression model	Regression model
Keletkezés éve	2010s	2002
Megalkotó	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Fotheringham, Brunsdon & Charlton
Típus	Quasi-experimental / causal inference	Local spatial regression
Alapmű	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Alternatív nevek	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Kapcsolódó	6	5
Összefoglaló	Spatial Inverse Probability Weighting extends the classical IPW estimator to settings where units are geo-referenced and spatial location is a confounding dimension. By incorporating geographic coordinates or spatial proximity into the propensity score model, it reweights the observed sample so that treatment and control groups are balanced not only on measured covariates but also on spatial structure, enabling credible causal inference from spatially indexed observational 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.