방법 비교

	인과관계에 대한 머신러닝 증강 민감도 분석	합성 통제 방법 (SCM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2018-2020	2010
창시자	Cinelli & Hazlett (sensitivity framework); Chernozhukov et al. (ML augmentation for causal estimation)	Abadie, Diamond & Hainmueller
유형	Sensitivity analysis / causal robustness assessment	Counterfactual causal-inference model
원전	Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.	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 sensitivity analysis, ML sensitivity analysis for causality, machine learning sensitivity analysis, debiased ML sensitivity analysis	synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)
관련	5	5
요약	Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity tools, it delivers both high-dimensional covariate adjustment and transparent communication of remaining uncertainty about unobserved confounders.	The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.