Comparar métodos

	Método Dinâmico de Controle Sintético	Avaliação de Impacto Contrafactual (AIC)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2010	1970s–2000s
Autor original	Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others	Heckman, Imbens, Rubin, and the program evaluation literature
Tipo	Comparative case study / counterfactual estimation	Causal inference / program evaluation
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.	Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.
Outros nomes	Dynamic SCM, Time-varying synthetic control, Multi-period synthetic control, DSC	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Relacionados	5	5
Resumo	The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically.	Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.