Bandingkan kaedah

	Anggaran Keboleh-kukuhan Ganda Angkasa	Regresi Berbobot Geografi (GWR)
Bidang	Inferens Kausal	Analisis Reruang
Keluarga	Regression model	Regression model
Tahun asal	2010s–2020s	2002
Pengasas	Extension of Robins, Rotnitzky & Zhao (1994) doubly robust framework to spatial settings; developed in spatial epidemiology and econometrics literature	Fotheringham, Brunsdon & Charlton
Jenis	Semiparametric causal estimator	Local spatial regression
Sumber perintis	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
Alias	Spatial DR, Spatial AIPW, Spatial augmented IPW, Doubly robust spatial causal estimation	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Berkaitan	5	5
Ringkasan	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.