方法证据记录
Machine learning-augmented doubly robust estimation
Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Machine Learning-Augmented Doubly Robust Estimation
分类方法记录 · regression-model / causal-inference
- 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
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