So sánh phương pháp

	Trọng số xác suất nghịch đảo không gian (Spatial IPW)	Hồi quy không gian (Mô hình độ trễ không gian và Mô hình sai số không gian)
Lĩnh vực	Suy luận nhân quả	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	2010s	1988
Người khởi xướng	Extension of Rosenbaum & Rubin (1983) IPW to spatial settings; formal treatment by Papadogeorgou et al. (2019)	Luc Anselin
Loại	Quasi-experimental / causal inference	Spatial regression (cross-sectional)
Công trình gốc	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.
Tên gọi khác	Spatial IPW, Geographic IPW, Spatially-weighted IPW, SIPW	spatial econometrics, spatial lag model, spatial error model, SAR / SEM
Liên quan	6	5
Tóm tắt	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.