เปรียบเทียบวิธี

	การวิเคราะห์ผลกระทบเชิงสาเหตุแบบหลายช่วงเวลา	การวิเคราะห์ผลกระทบเชิงสาเหตุ
สาขาวิชา	การอนุมานเชิงสาเหตุ	การอนุมานเชิงสาเหตุ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2015 (base); multi-period extensions 2017–present	2015
ผู้ริเริ่ม	Brodersen, Gallusser, Koehler, Remy & Scott (Google); extended to multi-period settings by subsequent applied work	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
ประเภท	Bayesian structural time-series / quasi-experimental	Bayesian causal inference / counterfactual forecasting
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.	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.
ชื่อเรียกอื่น	multi-period CausalImpact, staggered causal impact, repeated-period causal impact, multi-wave CausalImpact	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
ที่เกี่ยวข้อง	6	5
สรุป	Multi-period Causal Impact Analysis extends the Bayesian structural time-series framework of Brodersen et al. (2015) to settings where an intervention occurs across multiple distinct periods, is applied at staggered times to different units, or where researchers wish to evaluate cumulative and period-specific effects within a single unified model. It builds a synthetic counterfactual from control covariates and projects it across each intervention window to quantify causal effects.	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.