Comparar métodos

	Análise de Impacto Causal para Avaliação de Políticas	Método do Controle Sintético (MCS)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	2015	2003–2010
Autor original	Brodersen, Gallusser, Koehler, Remy & Scott (2015); adapted for policy evaluation contexts	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipo	Bayesian counterfactual / time-series	Quasi-experimental causal inference
Fonte seminal	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	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	policy causal impact, BSTS policy evaluation, Bayesian policy impact assessment, CIA policy evaluation	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionados	6	4
Resumo	Policy Evaluation Causal Impact Analysis applies the Bayesian structural time-series (BSTS) framework of Brodersen et al. (2015) to estimate the causal effect of a policy intervention on aggregate outcomes. By constructing a synthetic counterfactual from pre-policy data and control covariates, it asks: what would have happened had the policy not been enacted? The difference between observed and predicted post-policy outcomes is the estimated policy effect.	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.