방법 비교

	다기간 인과 영향 분석	합성 통제 방법 (SCM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2015 (base); multi-period extensions 2017–present	2003–2010
창시자	Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
유형	Bayesian structural time-series / quasi-experimental	Quasi-experimental causal inference
원전	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	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	multi-period CausalImpact, staggered causal impact, repeated-period causal impact, multi-wave CausalImpact	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
관련	6	4
요약	Multi-period Causal Impact Analysis extends the Bayesian structural time-series framework of Brodersen et al. (2015) to settings where an intervention occurs across multiple distinct periods, is applied at staggered times to different units, or where researchers wish to evaluate cumulative and period-specific effects within a single unified model. It builds a synthetic counterfactual from control covariates and projects it across each intervention window to quantify causal effects.	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.