Comparar métodos

	Método de Controle Sintético em Pesquisa Educacional	Análise de Séries Temporais Interrompidas (ITS)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2003-2010	2002
Autor original	Alberto Abadie, Alexis Diamond, and Jens Hainmueller	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Tipo	Quasi-experimental causal inference	Quasi-experimental segmented regression
Fonte seminal	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.
Outros nomes	SCM in education, synthetic control, synthetic comparator, SCM	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Relacionados	5	5
Resumo	The Synthetic Control Method (SCM) estimates the causal effect of an education policy or intervention by constructing a weighted combination of untreated comparison units — the synthetic control — that closely mimics the treated unit's pre-intervention trajectory. Developed by Abadie, Diamond, and Hainmueller, it is especially valuable when only one or a small number of schools, districts, or countries receive a policy change and no natural comparison 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.