方法证据记录
Machine Learning-Augmented Entropy Balancing
Machine learning-augmented entropy balancing (ML-EB) combines Hainmueller's entropy balancing reweighting scheme with a machine-learning outcome model to produce a doubly-robust causal estimator. By jointly optimising covariate balance weights and a flexible predicted-outcome adjustment, ML-EB delivers consistent treatment-effect estimates even when either the weighting or the outcome model is misspecified, and it handles high-dimensional covariate spaces that classical entropy balancing cannot easily balance.
源记录
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Machine Learning-Augmented Entropy Balancing for Causal Inference
分类方法记录 · regression-model / causal-inference
- Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46. · DOI 10.1093/pan/mpr025
- Zhao, Q., & Percival, D. (2017). Entropy balancing is doubly robust. Journal of Causal Inference, 5(1), 20160010. · DOI 10.1515/jci-2016-0010
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