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English(EN) Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

新框架通过恢复潜在混淆因子来估计网络中的因果效应

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新的框架，用于在网络环境中估计因果效应，解决了违反网络无混淆假设的常见挑战。所提出的方法利用网络交互模式来恢复潜在混淆因子，并将其分为影响个体单元、其邻居或两者的混淆因子。该方法利用可识别的表示学习技术来估计网络效应，理论证明了混淆因子和网络效应的可识别性。实验结果证实了这种新方法的有效性。 AI

排序理由 关于因果效应估计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新框架通过恢复潜在混淆因子来估计网络中的因果效应

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关于因果效应估计新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Weilin Chen, Ruichu Cai, Jie Qiao, Yuguang Yan, Jos\'e Miguel Hern\'andez-Lobato ·

    在无网络混淆假设下进行网络干扰下的因果效应估计

    arXiv:2502.19741v4 Announce Type: replace Abstract: Estimating causal effects under networked interference from observational data is a crucial yet challenging problem. Most existing methods mainly rely on the networked unconfoundedness assumption, which guarantees the identifica…

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