So sánh phương pháp

	Phương pháp Kiểm soát Tổng hợp Đa kỳ	Phương pháp Kiểm soát Tổng hợp Dữ liệu Bảng
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2010-2021	2010
Người khởi xướng	Abadie, Diamond & Hainmueller (2010); extended to multi-period settings by Abadie (2021) and Ben-Michael et al. (2021)	Alberto Abadie, Alexis Diamond & Jens Hainmueller
Loại	Quasi-experimental causal inference	Causal inference / panel data
Công trình gốc	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.
Tên gọi khác	multi-period SCM, extended synthetic control, synthetic control with multiple treatment periods, staggered synthetic control	SCM panel, panel synthetic control, synthetic control estimator, comparative case study
Liên quan	5	5
Tóm tắt	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 panel data synthetic control method estimates the causal effect of an intervention on a single treated unit by constructing a data-driven weighted combination of untreated units — a synthetic control — that best reproduces the treated unit's pre-treatment outcome trajectory. The post-treatment gap between the treated unit and its synthetic counterpart is the estimated treatment effect.