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| 인과 충격 분석× | 베이지안 구조 시계열× | |
|---|---|---|
| 분야≠ | 인과추론 | 베이지안 |
| 계열≠ | Regression model | Bayesian methods |
| 기원 연도≠ | 2015 | 2014 |
| 창시자≠ | Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google) | Scott & Varian (2014); Brodersen et al. (2015) |
| 유형≠ | Bayesian causal inference / counterfactual forecasting | State-space model / Bayesian structural model |
| 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. 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 | CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis | BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model |
| 관련 | 5 | 5 |
| 요약≠ | Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals. | 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. |
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