手法を比較

	反実仮想による影響評価（CIE）	因果影響分析
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	1970s–2000s	2015
提唱者	Heckman, Imbens, Rubin, and the program evaluation literature	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
種類	Causal inference / program evaluation	Bayesian causal inference / counterfactual forecasting
原典	Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.	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	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
関連	5	5
概要	Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.	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.