Сравнение методов

	Метод синтетического контроля для нескольких периодов	Динамический метод синтетического контроля
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2010-2021	2010
Автор метода	Abadie, Diamond & Hainmueller (2010); extended to multi-period settings by Abadie (2021) and Ben-Michael et al. (2021)	Abadie, Diamond & Hainmueller (2010); dynamic extensions by Abadie (2021) and others
Тип	Quasi-experimental causal inference	Comparative case study / counterfactual estimation
Основополагающий источник	Abadie, A. (2021). Using synthetic controls: Feasibility, data requirements, and methodological aspects. Journal of Economic Literature, 59(2), 391-425.	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	multi-period SCM, extended synthetic control, synthetic control with multiple treatment periods, staggered synthetic control	Dynamic SCM, Time-varying synthetic control, Multi-period synthetic control, DSC
Связанные	5	5
Сводка	The multi-period synthetic control method extends the classic synthetic control framework to settings where treatment occurs across several distinct periods or where the researcher needs to track causal effects over a prolonged post-treatment window. It constructs a weighted combination of untreated units that reproduces the treated unit's pre-treatment trajectory, then uses that synthetic counterfactual across all post-treatment periods to estimate time-varying treatment effects.	The Dynamic Synthetic Control Method extends the classic synthetic control framework to evaluate treatments that unfold over multiple periods or change in intensity over time. It constructs a weighted combination of untreated units that matches the treated unit in pre-treatment outcomes, then traces the full time path of treatment effects period by period after the intervention — capturing not just an average effect but how the effect evolves dynamically.