Võrdle meetodeid

	Ruumiandmete tundlikuse analüüs põhjuslikkuse jaoks	Ruumi viivis mudel (SAR / ruumiline autoregressiivne)
Valdkond	Põhjuslik järeldamine	Ruumianalüüs
Perekond	Regression model	Regression model
Tekkeaasta	1988–2021 (developed progressively)	1988
Looja	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Anselin (textbook formalisation); LeSage & Pace
Tüüp	Sensitivity / robustness analysis	Spatial autoregressive regression
Algallikas	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.
Rööpnimetused	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Seotud	6	5
Kokkuvõte	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 Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.