Comparar métodos

	Análise Bayesiana de Impacto Causal	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 (Google)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipo	Bayesian causal inference / 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	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionados	4	4
Resumo	Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal 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.