เปรียบเทียบวิธี

	การทดสอบการควบคุมเชิงเบย์เซียน	การวิเคราะห์ผลกระทบเชิงสาเหตุแบบเบย์ (Bayesian Causal Impact Analysis)
สาขาวิชา	การอนุมานเชิงสาเหตุ	การอนุมานเชิงสาเหตุ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2010-2015	2015
ผู้ริเริ่ม	Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition)	Brodersen, Gallusser, Koehler, Remy & Scott (Google)
ประเภท	Robustness check / falsification test	Bayesian causal inference / time series
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	Bayesian falsification test, Bayesian permutation placebo, Bayesian robustness check, Bayesian in-time placebo	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis
ที่เกี่ยวข้อง	5	4
สรุป	The Bayesian Placebo Test is a falsification strategy for causal inference that applies Bayesian inference to placebo scenarios — either fake treatments in the pre-intervention period, on unaffected units, or at fictitious cut-offs — to verify that observed treatment effects cannot plausibly arise by chance or from a misspecified model. It integrates prior information and yields posterior distributions of placebo effects for direct probabilistic comparison.	Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal effect.