Σύγκριση μεθόδων

	Χωρική Διπλά Εύρωστη Εκτίμηση	Παλινδρόμηση Γεωγραφικά Σταθμισμένη (GWR)
Πεδίο	Αιτιακή Συμπερασματολογία	Χωρική Ανάλυση
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2010s–2020s	2002
Δημιουργός	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Fotheringham, Brunsdon & Charlton
Τύπος	Semiparametric causal estimator	Local spatial regression
Θεμελιώδης πηγή	Papadogeorgou, G., Mealli, F., & Zigler, C. M. (2019). Causal inference with interfering units for cluster and population level treatment allocation programs. Biometrics, 75(3), 778-787.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Εναλλακτικές ονομασίες	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Συναφείς	5	5
Σύνοψη	Spatial doubly robust estimation is a semiparametric causal inference method that combines propensity score weighting with outcome regression modeling — providing protection against misspecification of either component — while explicitly accounting for spatial autocorrelation among units. It extends the classical augmented inverse probability weighting (AIPW) estimator to settings where treatment assignment and outcomes are geographically clustered or spatially dependent.	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.