Comparar métodos

	Séries Temporais Interrompidas com Dados em Painel	Método do Controle Sintético (MCS)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2000s–2010s	2003–2010
Autor original	Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipo	Quasi-experimental causal inference	Quasi-experimental causal inference
Fonte seminal	Lopez Bernal, J., 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.	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.
Outros nomes	panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionados	5	4
Resumo	Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detecting changes in the level and slope of the outcome trajectory immediately following a clearly dated intervention.	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.