Сравнение на методи

	Синтетичен контролен метод (SCM)	Анализ на причинното въздействие
Област	Причинно-следствено заключение	Причинно-следствено заключение
Семейство	Regression model	Regression model
Година на възникване	2003–2010	2015
Създател	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Тип	Quasi-experimental causal inference	Bayesian causal inference / counterfactual forecasting
Основополагащ източник	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.
Други названия	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Свързани	4	5
Резюме	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.