เปรียบเทียบวิธี

	การวิเคราะห์ผลกระทบเชิงสาเหตุ	แบบจำลองอนุกรมเวลาโครงสร้างแบบเบย์เซียน
สาขาวิชา	การอนุมานเชิงสาเหตุ	เบย์
ตระกูล	Regression model	Bayesian methods
ปีกำเนิด	2015	2014
ผู้ริเริ่ม	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)	Scott & Varian (2014); Brodersen et al. (2015)
ประเภท	Bayesian causal inference / counterfactual forecasting	State-space model / Bayesian structural model
แหล่งต้นตำรับ	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.	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.
ชื่อเรียกอื่น	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis	BSTS, Bayesian Yapısal Zaman Serisi (BSTS), bayesian state-space model, causal impact model
ที่เกี่ยวข้อง	5	5
สรุป	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.