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	ロバスト因果影響分析	ベイジアン因果影響分析 (Bayesian Causal Impact Analysis)
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	2015	2015
提唱者	Brodersen, Gallusser, Koehler, Remy & Scott (foundational CausalImpact framework)	Brodersen, Gallusser, Koehler, Remy & Scott (Google)
種類	Bayesian causal inference with robustness validation	Bayesian causal inference / time series
原典	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.
別名	robust CausalImpact, sensitivity-augmented causal impact, causal impact with robustness checks, robust BSTS causal inference	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis
関連	5	4
概要	Robust Causal Impact Analysis extends the Bayesian structural time-series CausalImpact framework (Brodersen et al., 2015) by embedding systematic robustness checks — in-time placebo tests, in-space placebo controls, covariate sensitivity analysis, and prior sensitivity assessments — to verify that a detected intervention effect is genuine and not an artifact of model choices or coincidental data patterns.	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.