Sammenlign metoder

	Syntetisk Kontrol Metode til Politikvurdering	Instrumentalvariabel (IV) Metoden til Kausal Inferens
Fagområde	Kausal inferens	Sundhedsøkonomi
Familie	Regression model	Process / pipeline
Oprindelsesår	2003-2010	1990s (modern applications)
Ophavsperson	Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Causal inference / comparative case study	Method
Oprindelig kilde	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Aliasser	Synthetic Control Method, SCM, Synthetic Control, Abadie-Diamond-Hainmueller method	IV, two-stage least squares, TSLS, causal estimation
Relaterede	5	3
Resumé	The Synthetic Control Method (SCM) is a causal inference technique for evaluating the effect of a policy or intervention on a single treated unit — such as a region, country, or firm — by constructing a weighted combination of untreated comparison units that closely mirrors the treated unit before the intervention. Introduced by Abadie and Gardeazabal (2003) and formalized by Abadie, Diamond, and Hainmueller (2010), it provides a data-driven, transparent counterfactual for comparative case studies.	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.