Krahasoni metodat

	Pesha Inverse Probabilitare Hapësinore (Spatial IPW)	Regresioni Hapësinor (Modelet e Vonesës Hapësinore dhe Gabimit Hapësinor)
Fusha	Inferenca kauzale	Ekonometri
Familja	Regression model	Regression model
Viti i origjinës	2010s	1988
Krijuesi	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Luc Anselin
Lloji	Quasi-experimental / causal inference	Spatial regression (cross-sectional)
Burimi themelues	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.
Emërtime të tjera	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	spatial econometrics, spatial lag model, spatial error model, SAR / SEM
Të lidhura	6	5
Përmbledhja	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.