Regression modelQuasi-experimental / causal inference
贝叶斯双重差分法
贝叶斯双重差分法（Bayesian Difference-in-Differences, Bayesian DiD）将贝叶斯统计推断应用于经典的DiD设计，用关于处理效应的完整后验分布取代了频率学派的点估计。这不仅能估计因果效应，还能就其大小和不确定性做出连贯的概率陈述，在样本量较小或有信息量先验知识可用时尤其有用。
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来源
- Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529. link ↗
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Difference-in-Differences Estimator. ScholarGate. https://scholargate.app/zh/causal-inference/bayesian-difference-in-differences
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
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- 面板数据固定效应模型计量经济学↔ compare
- 合成控制法 (SCM)因果推断↔ compare