Võrdle meetodeid

	Ruumiandmete tundlikuse analüüs põhjuslikkuse jaoks	Instrumentaalmuutujate (IV) meetod kausaalse järelduse tegemiseks
Valdkond	Põhjuslik järeldamine	Terviseökonoomika
Perekond	Regression model	Process / pipeline
Tekkeaasta	1988–2021 (developed progressively)	1990s (modern applications)
Looja	Anselin (1988) for spatial diagnostics; Reich et al. (2021) for spatial causal frameworks	Angrist & Pischke (applied econometrics); rooted in econometric theory
Tüüp	Sensitivity / robustness analysis	Method
Algallikas	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht. ISBN: 978-9024737322	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Rööpnimetused	spatial causal sensitivity, spatial robustness checks, SSAC, spatial confounding sensitivity	IV, two-stage least squares, TSLS, causal estimation
Seotud	6	3
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.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.