Methoden vergleichen

	Synthetische Kontrollmethode (SCM)	Instrumentalvariablen-Methode (IV) zur Kausalinferenz
Fachgebiet	Kausale Inferenz	Gesundheitsökonomie
Familie	Regression model	Process / pipeline
Entstehungsjahr	2003–2010	1990s (modern applications)
Urheber	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Typ	Quasi-experimental causal inference	Method
Wegweisende Quelle	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.
Aliasnamen	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method	IV, two-stage least squares, TSLS, causal estimation
Verwandt	4	3
Zusammenfassung	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.	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.