Сравнение методов

	Анализ прерванных временных рядов (Interrupted Time Series, ITS)	Байесовские структурные временные ряды
Область	Причинно-следственный вывод	Байесовские методы
Семейство	Regression model	Bayesian methods
Год появления	2002	2014
Автор метода	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	Scott & Varian (2014); Brodersen et al. (2015)
Тип	Quasi-experimental segmented regression	State-space model / Bayesian structural model
Основополагающий источник	Bernal, J. L., 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.	Scott, S. L. & Varian, H. R. (2014). Predicting the Present with Bayesian Structural Time Series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1/2), 4–23.
Другие названия	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model
Связанные	5	5
Сводка	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.	Bayesian Structural Time Series (BSTS) is a state-space modelling framework, introduced by Scott and Varian (2014), that decomposes a time series into additive components — trend, seasonality, and regression — and estimates them jointly through Bayesian inference. It underpins Google's CausalImpact library and is a powerful tool for both forecasting and counterfactual causal analysis of interventions.