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New Proximal Survival Bridge learning framework tackles censored outcomes

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种名为Proximal Survival Bridge learning (PSB) 的新框架，该框架旨在估计受删失和未测量混淆影响的时间事件结果的因果效应。这种灵活的双重稳健方法利用了两种不同的桥接学习器：PSB-K（一种核估计器）和PSB-N（一种神经网络估计器）。PSB在各种基准测试中表现出色，包括标准的SEM基准、近端半合成ACTG研究以及Framingham Heart Study，即使在代理变量减弱或删失较重的情况下也表现出鲁棒性。 AI

影响 为生存分析中的因果推断引入了一种新的统计方法，有可能改善依赖时间事件数据的领域的研究。

排序理由 发布了一篇详细介绍新统计框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

New Proximal Survival Bridge learning framework tackles censored outcomes

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发布了一篇详细介绍新统计框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dhanajit Brahma, Ruolin Meng, Lawrence Carin, Ricardo Henao ·

    Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes

    arXiv:2610.20893v1 Announce Type: cross Abstract: Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding. Proximal causal inference addresses unmeasured confounding through proxy variables, but existing proximal survival met…

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