Sammenlign metoder

	Spatial Instrumental Variables (Spatial IV / Spatial 2SLS)	Instrumentelle variabler (IV) metode for kausal inferens
Fagfelt	Kausal inferens	Helseøkonomi
Familie	Regression model	Process / pipeline
Opprinnelsesår	1988-1998	1990s (modern applications)
Opphavsperson	Kelejian & Prucha (generalized spatial 2SLS); Anselin (spatial econometrics framework)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Quasi-experimental causal inference with spatial dependence	Method
Opprinnelig kilde	Kelejian, H. H., & Prucha, I. R. (1998). A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances. Journal of Real Estate Finance and Economics, 17(1), 99-121.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Alias	Spatial IV, Spatial 2SLS, Spatial Two-Stage Least Squares, S-IV	IV, two-stage least squares, TSLS, causal estimation
Relaterte	6	3
Sammendrag	Spatial Instrumental Variables (Spatial IV) is a causal inference method for settings where units — regions, firms, neighborhoods — are spatially interdependent, creating endogeneity that standard IV approaches ignore. It constructs instruments from the spatially lagged values of exogenous characteristics of neighboring units, then applies two-stage least squares to recover unbiased causal estimates in the presence of both endogenous regressors and spatial autocorrelation.	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.