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English(EN) Bayesian X-Learner: Calibrated Posterior Inference for Heterogeneous Treatment Effects under Heavy-Tailed Outcomes

Bayesian X-Learner 为异质性处理效应提供校准推断

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新的异质性处理效应估计方法——Bayesian X-Learner，该方法即使在处理重尾结果数据时也能进行校准不确定性估计。该方法在现有元学习器的基础上，通过引入完整的马尔可夫链蒙特卡洛后验和Welsch红降伪似然函数进行构建。该方法在IHDP基准测试中表现出竞争力，并在处理污染数据集方面显示出鲁棒性，实现了改进的RMSE和可信区间覆盖率。 AI

影响 引入了一种用于因果推断的鲁棒统计方法，有可能提高在数据嘈杂领域中AI驱动的决策的可靠性。

排序理由 这是一篇详细介绍因果推断新统计方法的学术论文。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Bayesian X-Learner 为异质性处理效应提供校准推断

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Eichi Uehara ·

    贝叶斯X-Learner：重尾结果下异质性处理效应的校准后验推断

    arXiv:2604.27394v1 Announce Type: new Abstract: Conditional Average Treatment Effect (CATE) estimation in practice demands three properties simultaneously: heterogeneous effects $\tau(x)$, calibrated uncertainty over them, and robustness to the heavy tails that contaminate real o…

  2. arXiv stat.ML TIER_1 English(EN) · Eichi Uehara ·

    贝叶斯X-Learner：重尾结果下异质性处理效应的校准后验推断

    Conditional Average Treatment Effect (CATE) estimation in practice demands three properties simultaneously: heterogeneous effects $τ(x)$, calibrated uncertainty over them, and robustness to the heavy tails that contaminate real outcome data. Meta-learners (Künzel et al., 2019) gi…

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