方法对比

	贝叶斯反事实影响评估	反事实影响评估 (CIE)
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	2015 (canonical implementation); Rubin potential outcomes: 1974-2005	1970s–2000s
提出者	Brodersen, Gallusser, Koehler, Remy & Scott; Rubin potential outcomes framework	Heckman, Imbens, Rubin, and the program evaluation literature
类型	Bayesian causal inference / counterfactual estimation	Causal inference / program evaluation
开创性文献	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.	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.
别名	Bayesian CIE, Bayesian causal impact, Bayesian structural time-series causal inference, BSTS counterfactual evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
相关	5	5
摘要	Bayesian Counterfactual Impact Evaluation estimates the causal effect of an intervention by constructing a Bayesian posterior distribution over the counterfactual outcome — what would have happened without treatment. The method, popularized by Brodersen et al. (2015) through the CausalImpact framework, uses Bayesian structural time-series models fitted on the pre-intervention period to predict the counterfactual trajectory, then compares observed post-intervention outcomes to that prediction.	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.