Comparer des méthodes

	Méthode du Contrôle Synthétique (SCM)	Analyse de séries chronologiques interrompues (ITS)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	2010	2002
Auteur d'origine	Abadie, Diamond & Hainmueller	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Type	Counterfactual causal-inference model	Quasi-experimental segmented regression
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.	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
Alias	synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Apparentées	5	5
Résumé	The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.