방법 비교

	베이지안 반사실적 영향 평가	반사실적 영향 평가 (CIE)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2015 (canonical implementation); Rubin potential outcomes: 1974-2005	1970s–2000s
창시자	Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework	Heckman, Imbens, Rubin, and the program evaluation literature
유형	Bayesian causal inference / counterfactual estimation	Causal inference / program evaluation
원전	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.	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.
별칭	Bayesian CIE, Bayesian causal impact, Bayesian structural time-series causal inference, BSTS counterfactual evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
관련	5	5
요약	Bayesian Counterfactual Impact Evaluation estimates the causal effect of an intervention by constructing a Bayesian posterior distribution over the counterfactual outcome — what would have happened without treatment. The method, popularized by Brodersen et al. (2015) through the CausalImpact framework, uses Bayesian structural time-series models fitted on the pre-intervention period to predict the counterfactual trajectory, then compares observed post-intervention outcomes to that prediction.	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.