Сравнение методов

	Машинно-обучаемый аугментированный метод синтетического контроля	Анализ причинно-следственного воздействия (Causal Impact Analysis)
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2021	2015
Автор метода	Ben-Michael, Feller & Rothstein	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Тип	Causal inference / quasi-experimental	Bayesian causal inference / counterfactual forecasting
Основополагающий источник	Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The augmented synthetic control method. Journal of the American Statistical Association, 116(536), 1789-1803.	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.
Другие названия	ML-augmented SCM, augmented synthetic control, ASC, penalized synthetic control	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Связанные	5	5
Сводка	The machine learning-augmented synthetic control method extends the classical synthetic control estimator by using penalized regression or other ML algorithms — such as lasso, ridge, or random forests — to construct the donor weights and to model pre-treatment outcome trajectories. The augmentation corrects for residual imbalance left by the standard weighting step, yielding lower bias when no perfect synthetic control exists.	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.