방법 비교

	머신러닝 증강 인과적 영향 분석	합성 통제 방법 (SCM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2015-2018	2003–2010
창시자	Brodersen et al. (foundational BSTS framework, 2015); Chernozhukov et al. (double ML augmentation, 2018)	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
유형	Quasi-experimental causal inference with ML	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	ML-augmented causal impact, ML-CausalImpact, machine learning causal impact, ML-augmented BSTS	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
관련	6	4
요약	Machine learning-augmented causal impact analysis combines quasi-experimental counterfactual reasoning with flexible ML prediction models to estimate the causal effect of an intervention on a time series outcome. Building on Brodersen et al.'s Bayesian structural time series (BSTS) framework and extended by double/debiased ML methods, it constructs a synthetic counterfactual from donor covariates and infers the treatment effect as the gap between observed and predicted post-intervention outcomes.	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.