Salīdzināt metodes

	Telpiskās apgrieztās varbūtības svēršana (Telpiskā IPW)	Telpiskā regresija (telpiskā nobīdes un telpiskās kļūdas modeļi)
Nozare	Cēloņsakarību secināšana	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	2010s	1988
Autors	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Luc Anselin
Tips	Quasi-experimental / causal inference	Spatial regression (cross-sectional)
Pirmavots	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.
Citi nosaukumi	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	spatial econometrics, spatial lag model, spatial error model, SAR / SEM
Saistītās	6	5
Kopsavilkums	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.