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机器学习增强熵平衡法

机器学习增强熵平衡法（ML-EB）将Hainmueller的熵平衡重加权方案与机器学习结果模型相结合，以产生一个双重稳健的因果估计量。通过联合优化协变量平衡权重和一个灵活的预测结果调整，即使权重模型或结果模型被错误指定，ML-EB也能提供一致的治疗效应估计，并且它能处理经典熵平衡法难以平衡的高维协变量空间。

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机器学习增强熵平衡法
双重稳健估计（AIPW）熵平衡逆概率治疗加权法 (IPW / IPTW)倾向得分匹配

来源

  1. 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 ↗
  2. Zhao, Q., & Percival, D. (2017). Entropy balancing is doubly robust. Journal of Causal Inference, 5(1), 20160010. DOI: 10.1515/jci-2016-0010 ↗

如何引用本页

ScholarGate. (2026, June 3). Machine Learning-Augmented Entropy Balancing for Causal Inference. ScholarGate. https://scholargate.app/zh/causal-inference/machine-learning-augmented-entropy-balancing

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ScholarGate — Machine Learning-Augmented Entropy Balancing (Machine Learning-Augmented Entropy Balancing for Causal Inference). 于 2026-06-15 检索自 https://scholargate.app/zh/causal-inference/machine-learning-augmented-entropy-balancing · 数据集： https://doi.org/10.5281/zenodo.20539026