পদ্ধতির তুলনা করুন

	মেশিন লার্নিং-বর্ধিত ব্যাহত সময় সিরিজ	ডাইনামিক ইন্টারাপ্টেড টাইম সিরিজ (Dynamic Interrupted Time Series)
ক্ষেত্র	কার্যকারণ অনুমান	কার্যকারণ অনুমান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2014-2015	2002–2017
প্রবর্তক	Brodersen et al. (2015); Varian (2014) — foundational ML-for-causal-inference literature	Wagner, Soumerai, Zhang & Ross-Degnan; extended by Lopez Bernal, Cummins & Gasparrini
ধরন	Quasi-experimental causal inference with ML counterfactual	Quasi-experimental time-series design
মৌলিক উৎস	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.
অপর নাম	ML-ITS, ML-augmented ITS, machine learning ITS, causal ML interrupted time series	Dynamic ITS, ITS with lagged effects, time-varying ITS, flexible ITS
সম্পর্কিত	6	4
সারসংক্ষেপ	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.