So sánh phương pháp

	Phân tích chuỗi thời gian bị gián đoạn (ITS)	Chuỗi thời gian cấu trúc Bayes
Lĩnh vực	Suy luận nhân quả	Bayes
Họ	Regression model	Bayesian methods
Năm ra đời	2002	2014
Người khởi xướng	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	Scott & Varian (2014); Brodersen et al. (2015)
Loại	Quasi-experimental segmented regression	State-space model / Bayesian structural model
Công trình gốc	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.
Tên gọi khác	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
Liên quan	5	5
Tóm tắt	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.