방법 비교

	강건한 인과 영향 분석	합성 통제 방법 (SCM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2015	2003–2010
창시자	Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
유형	Bayesian causal inference with robustness validation	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	robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
관련	5	4
요약	Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity analysis, and prior sensitivity assessments — to verify that a detected intervention effect is genuine and not an artifact of model choices or coincidental data patterns.	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.