方法对比

	合成控制法用于政策评估	中断时间序列（ITS）分析
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2003-2010	2002
提出者	Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
类型	Causal inference / comparative case study	Quasi-experimental segmented regression
开创性文献	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.	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
别名	Synthetic Control Method, SCM, Synthetic Control, Abadie-Diamond-Hainmueller method	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
相关	5	5
摘要	The Synthetic Control Method (SCM) is a causal inference technique for evaluating the effect of a policy or intervention on a single treated unit — such as a region, country, or firm — by constructing a weighted combination of untreated comparison units that closely mirrors the treated unit before the intervention. Introduced by Abadie and Gardeazabal (2003) and formalized by Abadie, Diamond, and Hainmueller (2010), it provides a data-driven, transparent counterfactual for comparative case studies.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.