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English(EN) Transportable Causal Effect Estimation across Networks under Interference

新研究应对网络化系统中的因果推断挑战

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了在效应可能传播或相互干扰的复杂系统中的因果推断。第一篇论文《具有学习暴露映射的干扰因果推断》研究了学习到的传输过程中的不确定性如何影响溢出效应估计，并比较了机械模型与PINO和FNO等算子学习方法。第二篇论文《跨网络干扰下的可迁移因果效应估计》介绍了TranCE算法，该算法旨在估计一个网络人群中的因果效应并将其迁移到另一个网络，以解决协变量偏移和网络结构差异等挑战。 AI

影响 这些论文推进了理解和预测复杂互联系统中干预措施影响的方法，有可能改进社交网络和公共卫生等领域的策略。

排序理由 两篇在arXiv上发表的学术论文，提出了复杂系统因果推断的新方法。

在 arXiv cs.LG 阅读 →

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

新研究应对网络化系统中的因果推断挑战

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两篇在arXiv上发表的学术论文，提出了复杂系统因果推断的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Cong Cao ·

    在学习暴露映射下的因果推断与干扰

    arXiv:2608.19224v1 Announce Type: cross Abstract: Exposure mappings are often assumed to be known in causal spillover analyses. In environmental settings, however, they are typically induced by transport processes that are not directly observed and must instead be learned from po…

  2. arXiv cs.LG TIER_1 English(EN) · Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le ·

    跨网络干扰下的可运输因果效应估计

    arXiv:2608.18932v1 Announce Type: new Abstract: Estimating causal effects under network interference typically assumes that the network used for training and the network used for deployment coincide. In practice, an intervention is run on one population while the question of inte…

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