Сравнение методов

	Машинно-обучаемый аугментированный метод синтетического контроля	Разность разностей (Difference-in-Differences, DiD)
Область	Причинно-следственный вывод	Эконометрика
Семейство	Regression model	Regression model
Год появления	2021	1994
Автор метода	Ben-Michael, Feller & Rothstein	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Тип	Causal inference / quasi-experimental	Causal inference / panel regression
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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Другие названия	ML-augmented SCM, augmented synthetic control, ASC, penalized synthetic control	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.