Сравнение методов

	Анализ причинно-следственного влияния на панельных данных	Синтетический метод контроля (SCM)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2015 (base method); panel extension mid-2010s	2003–2010
Автор метода	Brodersen et al. (2015); panel extension by Holtz et al. and subsequent literature	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Тип	Bayesian structural time-series causal inference	Quasi-experimental causal inference
Основополагающий источник	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.
Другие названия	Panel CausalImpact, multi-unit causal impact, panel BSTS causal inference, panel structural time-series causal analysis	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Связанные	6	4
Сводка	Panel data causal impact analysis extends the Bayesian structural time-series approach of Brodersen et al. (2015) to multi-unit panel settings, estimating the counterfactual for several treated units simultaneously using control units as a donor pool. It produces credible intervals for the causal effect at each post-intervention time point, aggregated across units and periods.	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.