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| 시계열 단절 분석 (Interrupted Time Series, ITS)× | 베이지안 구조 시계열× | |
|---|---|---|
| 분야≠ | 인과추론 | 베이지안 |
| 계열≠ | Regression model | Bayesian methods |
| 기원 연도≠ | 2002 | 2014 |
| 창시자≠ | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) | Scott & Varian (2014); Brodersen et al. (2015) |
| 유형≠ | Quasi-experimental segmented regression | State-space model / Bayesian structural model |
| 원전≠ | 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. DOI ↗ | 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. DOI ↗ |
| 별칭≠ | 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 |
| 관련 | 5 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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