So sánh phương pháp

	Phân tích Tác động Nhân quả Không gian	Hồi quy Trọng số Địa lý (GWR)
Lĩnh vực	Suy luận nhân quả	Phân tích không gian
Họ	Regression model	Regression model
Năm ra đời	2010s (codified)	2002
Người khởi xướng	Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988)	Fotheringham, Brunsdon & Charlton
Loại	Quasi-experimental causal inference with spatial data	Local spatial regression
Công trình gốc	Delgado, M. S., & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123-126.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Tên gọi khác	spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Liên quan	4	5
Tóm tắt	Spatial causal impact analysis estimates the causal effect of a spatially-targeted intervention — a policy, shock, or treatment applied to particular locations — while explicitly accounting for geographic spillovers between treated and untreated units. By combining quasi-experimental designs such as difference-in-differences or regression discontinuity with spatial econometric models, it separates the direct local effect of a treatment from indirect effects that diffuse to neighbouring areas.	Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships.