Regression modelQuasi-experimental / causal inference
机器学习增强的事件研究设计
机器学习增强的事件研究设计将标准的事件研究框架——追踪结果围绕处理日期的动态变化——与机器学习方法（如双重/无偏机器学习（DML）或正则化回归）相结合，以处理高维协变量，改善混淆因子控制，并在协变量空间过大以至于传统回归无法可靠管理时，产生有效的因果估计。
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来源
- Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI: 10.1111/ectj.12097 ↗
- Athey, S., & Imbens, G. W. (2022). Design-based analysis in difference-in-differences settings with staggered adoption. Journal of Econometrics, 226(1), 62-79. DOI: 10.1016/j.jeconom.2020.10.012 ↗
如何引用本页
ScholarGate. (2026, June 3). Machine Learning-Augmented Event Study Design. ScholarGate. https://scholargate.app/zh/causal-inference/machine-learning-augmented-event-study-design
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