방법 비교

	머신러닝 강화 중단 시계열 분석	동적 단절 시계열 분석 (Dynamic Interrupted Time Series)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2014-2015	2002–2017
창시자	Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature	Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini
유형	Quasi-experimental causal inference with ML counterfactual	Quasi-experimental time-series design
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.	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.
별칭	ML-ITS, ML-augmented ITS, machine learning ITS, causal ML interrupted time series	Dynamic ITS, ITS with lagged effects, time-varying ITS, flexible ITS
관련	6	4
요약	Machine Learning-Augmented Interrupted Time Series (ML-ITS) estimates the causal effect of a discrete intervention by training a machine learning model on pre-intervention time series data, projecting a counterfactual trajectory into the post-intervention period, and measuring the gap between observed and predicted outcomes. It extends classical ITS by replacing parametric trend assumptions with flexible ML estimators such as gradient boosting, random forests, or Bayesian structural time-series models.	Dynamic Interrupted Time Series (Dynamic ITS) extends the standard ITS design by allowing intervention effects to build up, decay, or shift over multiple time lags rather than assuming a single instantaneous level change. It estimates how an intervention's impact evolves across time periods, making it especially suited to public health, health services research, and policy evaluation where effects accumulate gradually or wear off after initial impact.