English(EN) Causal DAG Identification for Count Data via Poisson Thinning Structural Equation Models

新的统计模型可从计数数据中识别因果有向无环图

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员引入了一个名为泊松稀疏结构方程模型（PT-SEM）的新统计框架，用于从观察性计数数据中识别因果有向无环图（DAG）。该模型通过利用泊松稀疏并允许外生变量具有各种计数分布，扩展了现有的泊松分支结构因果模型（PB-SCM）。PT-SEM框架在特定条件下确立了因果DAG、稀疏系数和外生分布的可识别性，在某些场景下提供了完全可识别性。还开发了一种基于动态规划和BIC分数的结构学习算法，该算法在模拟和实际应用中均表现出强大的性能。 AI

影响为涉及计数数据的AI和机器学习研究中的因果推断提供了一种新的方法工具。

排序理由介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Penggang Gao, Ming Cai, Hisayuki Hara · 2026-09-10 04:00

基于泊松稀疏结构方程模型的计数数据因果有向无环图识别

arXiv:2609.06098v1 Announce Type: cross Abstract: Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs from observational data remain limited. The Poisson branching structural causal mod…

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基于泊松稀疏结构方程模型的计数数据因果有向无环图识别

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