방법 비교

	합성 통제 방법 (SCM)	인과 충격 분석
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2003–2010	2015
창시자	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
유형	Quasi-experimental causal inference	Bayesian causal inference / counterfactual forecasting
원전	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.	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.
별칭	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
관련	4	5
요약	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.	Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.