Regression modelQuasi-experimental / causal inference
贝叶斯因果效应分析
贝叶斯因果效应分析（Bayesian Causal Impact Analysis）利用贝叶斯结构时间序列（BSTS）模型来估计干预对时间序列结果变量的因果效应。该方法由Google的Brodersen及其同事于2015年开发，它构建了一个概率性的反事实——即在没有干预的情况下时间序列会呈现的样子——该反事实基于干预前的数据和可选的控制协变量，然后将其与观察到的干预后值进行比较，从而得到关于因果效应的完全贝叶斯后验分布。
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来源
- 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. DOI: 10.1214/14-AOAS788 ↗
- Scott, S. L., & Varian, H. R. (2014). Predicting the present with Bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1-2), 4-23. DOI: 10.1504/IJMMNO.2014.059942 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Causal Impact Analysis via Structural Time Series. ScholarGate. https://scholargate.app/zh/causal-inference/bayesian-causal-impact-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 因果影响分析因果推断↔ compare
- 双重差分法 (Diff-in-Diff)计量经济学↔ compare
- 中断时间序列（ITS）分析因果推断↔ compare
- 合成控制法 (SCM)因果推断↔ compare