# Learning linear non-Gaussian directed acyclic graph with diverging number of nodes

@article{Zhao2021LearningLN, title={Learning linear non-Gaussian directed acyclic graph with diverging number of nodes}, author={Ruixuan Zhao and Xin He and Junhui Wang}, journal={ArXiv}, year={2021}, volume={abs/2111.00740} }

Acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. This is in sharp contrast to most existing DAG learning methods assuming Gaussian noise with additional variance assumptions to attain exact DAG recovery. The proposed…

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