Machine learningCausal ML
双重机器学习
双重/去偏机器学习（DML）由Chernozhukov等人（2018）提出，是一种在存在高维控制变量的情况下估计因果或结构参数的半参数框架。它使用灵活的机器学习方法来建模扰动函数——结果和处理条件于协变量的期望——然后构建一个目标参数的去偏估计量，该估计量尽管存在高维设置固有的正则化偏差，但仍能实现根号n一致性和有效的推断。
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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 ↗
如何引用本页
ScholarGate. (2026, June 2). Double/Debiased Machine Learning (DML). ScholarGate. https://scholargate.app/zh/causal-inference/double-machine-learning
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