Scalable Causal Graph Learning through a Deep Neural Network
Scalable Causal Graph Learning through a Deep Neural Network
复制标题
通过深度神经网络进行可扩展因果图学习
DOI：
10.1145/3357384.3357864
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shinjae Yoo
中科院分区：
文献类型：
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作者：
Chenxiao Xu;Hao Huang;Shinjae Yoo
学习复杂系统中的因果图对于知识发现和决策制定至关重要，但由于系统组件之间未知的非线性相互作用，它仍然是一个具有挑战性的问题。大多数现有方法要么依赖于预定义的内核或数据分布，要么仅关注单个目标与其余系统之间的因果关系。这项工作提出了一种通过低秩近似进行可扩展因果图学习（SCGL）的深度神经网络。 SCGL 模型可以探索时间关系和互变量关系的非线性，无需任何预定义的核或分布假设。通过低秩逼近，降低了噪声影响，实现了更好的精度和高可扩展性。使用合成数据集和真实数据集的实验表明，我们的 SCGL 算法优于现有最先进的因果图学习方法。
Learning the causal graph in a complex system is crucial for knowledge discovery and decision making, yet it remains a challenging problem because of the unknown nonlinear interaction among system components. Most of the existing methods either rely on predefined kernel or data distribution, or they focus simply on the causality between a single target and the remaining system. This work presents a deep neural network for scalable causal graph learning (SCGL) through low-rank approximation. The SCGL model can explore nonlinearity on both temporal and intervariable relationships without any predefined kernel or distribution assumptions. Through low-rank approximation, the noise influence is reduced, and better accuracy and high scalability are achieved. Experiments using synthetic and real-world datasets show that our SCGL algorithm outperforms existing state-of-the-art methods for causal graph learning.