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	स्थानिक व्युत्क्रम संभाव्यता भारण (स्थानिक IPW)	स्थानिक प्रतिगमन (स्थानिक लैग और स्थानिक त्रुटि मॉडल)
क्षेत्र	कारणात्मक अनुमान	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	2010s	1988
प्रवर्तक	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Luc Anselin
प्रकार	Quasi-experimental / causal inference	Spatial regression (cross-sectional)
मौलिक स्रोत	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.
उपनाम	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	spatial econometrics, spatial lag model, spatial error model, SAR / SEM
संबंधित	6	5
सारांश	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.