Salīdzināt metodes

	Telpiskā kauzālā ietekmes analīze	Ģeogrāfiski svērtā regresija (GWR)
Nozare	Cēloņsakarību secināšana	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	2010s (codified)	2002
Autors	Delgado & Florax (spatial DiD); Halleck Vega & Elhorst (SLX model); broader lineage in spatial econometrics (Anselin, 1988)	Fotheringham, Brunsdon & Charlton
Tips	Quasi-experimental causal inference with spatial data	Local spatial regression
Pirmavots	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
Citi nosaukumi	spatial causal inference, geo-causal analysis, spatial treatment effect estimation, spatial impact evaluation	GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR)
Saistītās	4	5
Kopsavilkums	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.