Salīdzināt metodes

	Telpiskās jutīguma analīze cēloniskumam	Telpiskais kļūdu modelis (SEM)
Nozare	Cēloņsakarību secināšana	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1988–2021 (developed progressively)	1988
Autors	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Anselin
Tips	Sensitivity / robustness analysis	Spatial regression (spatially autocorrelated errors)
Pirmavots	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Citi nosaukumi	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	SEM, spatial error regression, spatial autoregressive error model, Uzamsal Hata Modeli (SEM / Spatial Error)
Saistītās	6	5
Kopsavilkums	Spatial sensitivity analysis for causality systematically tests whether a causal estimate derived from georeferenced data holds up as spatial structure, spillovers, and the choice of spatial weights matrix are varied. Because nearby units often share unmeasured confounders — soil quality, local infrastructure, neighbourhood norms — a naive regression may yield biased causal estimates. This method reveals how fragile or robust a claimed causal effect is to alternative spatial specifications.	The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move together, and it is estimated by maximum likelihood or GMM rather than ordinary least squares.