방법 비교

	패널 데이터 단절 시계열	합성 통제 방법 (SCM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2000s–2010s	2003–2010
창시자	Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
유형	Quasi-experimental causal inference	Quasi-experimental causal inference
원전	Lopez Bernal, J., 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.	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.
별칭	panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
관련	5	4
요약	Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detecting changes in the level and slope of the outcome trajectory immediately following a clearly dated intervention.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.