Comparer des méthodes

	Méthode du Contrôle Synthétique (MCS)	Méthode des variables instrumentales (VI) pour l'inférence causale
Domaine	Inférence causale	Économie de la santé
Famille	Regression model	Process / pipeline
Année d'origine	2003–2010	1990s (modern applications)
Auteur d'origine	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Type	Quasi-experimental causal inference	Method
Source fondatrice	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.
Alias	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method	IV, two-stage least squares, TSLS, causal estimation
Apparentées	4	3
Résumé	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.