方法对比

	贝叶斯事件研究设计	因果影响分析
领域	因果推断	因果推断
方法族	Regression model	Regression model
起源年份	1990s–2010s	2015
提出者	Developed from classical event study methodology (Fama et al., 1969) with Bayesian extensions proposed through the 1990s–2010s	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
类型	Quasi-experimental / causal inference	Bayesian causal inference / counterfactual forecasting
开创性文献	Sorescu, A., Warren, N. L., & Ertekin, L. (2017). Event study methodology in the marketing literature: An overview. Journal of the Academy of Marketing Science, 45(2), 186-207.	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.
别名	Bayesian event study, Bayesian abnormal return estimation, Bayesian pre-post event analysis, BES	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
相关	5	5
摘要	Bayesian Event Study Design extends the classical event study framework by replacing frequentist significance testing with a full Bayesian inferential framework. It estimates how an event (policy change, announcement, shock) alters an outcome trajectory by learning a prior model from the estimation window and updating it with observed data, yielding posterior distributions over abnormal effects and cumulative causal impacts with full uncertainty quantification.	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.