Mainly motivated by the problem of modelling directional dependence relationships for multivariate count data in high-dimensional settings, we present a new algorithm, called learnDAG, for learning the structure of directed acyclic graphs (DAGs). In particular, the proposed algorithm tackled the problem of learning DAGs from observational data in two main steps: (i) estimation of candidate parent sets; and (ii) feature selection. We experimentally compare learnDAG to several popular competitors in recovering the true structure of the graphs in situations where relatively moderate sample sizes are available. Furthermore, to make our algorithm is stronger, a validation of the algorithm is presented through the analysis of real datasets.

翻译：主要受高维环境下多元计数数据方向性依赖关系建模问题的启发，本文提出了一种名为learnDAG的新算法，用于学习有向无环图（DAGs）的结构。该算法通过两个核心步骤解决从观测数据中学习DAG的问题：（i）候选父节点集的估计；（ii）特征选择。我们在中等样本量条件下，通过实验将learnDAG与多种主流算法在图结构真实恢复能力方面进行了比较。此外，为增强算法的可靠性，我们通过对真实数据集的分析对算法进行了验证。