Bandingkan metode

	Pembobotan Probabilitas Terbalik Spasial (Spatial IPW)	Regresi Spasial (Model Lag Spasial dan Error Spasial)
Bidang	Inferensi Kausal	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	2010s	1988
Pencetus	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Luc Anselin
Tipe	Quasi-experimental / causal inference	Spatial regression (cross-sectional)
Sumber perintis	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers.
Alias	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	spatial econometrics, spatial lag model, spatial error model, SAR / SEM
Terkait	6	5
Ringkasan	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.	Spatial regression is a family of regression models that build geographic neighbourhood relationships directly into the model, introduced by Luc Anselin in his 1988 treatment of spatial econometrics. It splits into a spatial lag model, where spatial dependence sits in the dependent variable, and a spatial error model, where the dependence sits in the error term.