Regression modelQuasi-experimental / causal inference
机器学习增强双重稳健估计 (ML-DR)
机器学习增强双重稳健 (ML-DR) 估计将经典的双重稳健 (AIPW) 识别策略与用于处理混淆函数（倾向得分和结果回归）的灵活机器学习模型相结合。其结果是一种因果估计量，如果任一机器学习组件被正确指定，则该估计量是一致的，并且即使混淆函数模型使用高维正则化或非参数学习器进行估计，也能实现有效的根号n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 ↗
- Farrell, M. H., Liang, T., & Misra, S. (2021). Deep Neural Networks for Estimation and Inference. Econometrica, 89(1), 181-213. DOI: 10.3982/ECTA16901 ↗
如何引用本页
ScholarGate. (2026, June 3). Machine Learning-Augmented Doubly Robust Estimation. ScholarGate. https://scholargate.app/zh/causal-inference/machine-learning-augmented-doubly-robust-estimation
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.
- 双重差分法 (Diff-in-Diff)计量经济学↔ compare
- 双重稳健估计（AIPW）因果推断↔ compare
- 逆概率治疗加权法 (IPW / IPTW)因果推断↔ compare
- 机器学习增强倾向得分匹配因果推断↔ compare
- Marginal Structural Model (MSM)因果推断↔ compare
- 倾向得分加权法 (PSW / IPW)因果推断↔ compare