Methoden vergelijken

	Synthetische Controle Methode voor Beleidsevaluatie	Onderbroken Tijdreeks (ITS) Analyse
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2003-2010	2002
Grondlegger	Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Type	Causal inference / comparative case study	Quasi-experimental segmented regression
Oorspronkelijke bron	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.
Aliassen	Synthetic Control Method, SCM, Synthetic Control, Abadie-Diamond-Hainmueller method	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Verwant	5	5
Samenvatting	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.	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.