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English(EN) Network Effect Decomposition for Causal Effect Estimation under Network Interference

NEDNet方法分解网络效应以进行因果推断

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了NEDNet，一种新颖的网络效应分解方法，用于估计观测网络数据中的因果效应。该方法通过参数化条件结果响应为不同的路径，将个体处理效应与邻域暴露效应分离开来。在源自BlogCatalog和Flickr的半合成数据集上进行的实验表明，NEDNet在各种响应设置下实现个体、同伴和总效应的最低预测误差方面表现优越。 AI

影响 引入了一种用于网络数据因果推断的新方法，有可能提高对处理和暴露效应理解的准确性。

排序理由 该集群包含一篇详细介绍因果效应估计新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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

NEDNet方法分解网络效应以进行因果推断

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该集群包含一篇详细介绍因果效应估计新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Zhirong Huang, Wenzhen Zhang, Guangquan Lu, Boyan Chen, Debo Cheng, Shichao Zhang ·

    网络效应分解用于网络干预下的因果效应估计

    arXiv:2611.07879v1 Announce Type: new Abstract: Estimating causal effects from observational network data requires accounting for both individual treatment and neighborhood exposure. Existing outcome models commonly incorporate these intervention variables into a joint predictor …

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