To examine the structural learning of the additive noise model in causal discovery, a new algorithm, i.e., RCB (Rank-Correlation-Based), is proposed in combination with the method of Rank Correlation. This algorithm can effectively process multivariate linear Gaussian, non-Gaussian and multivariate nonlinear non-Gaussian data. In this article we have made three contributions. First, it is proven that rank correlation can be used as the criterion of the independence test. Second, through a series of experiments, the optimal threshold of rank correlation is found to select the potential neighbors of the target node. Thus, the RCB algorithm greatly reduces the search space and achieves good time performance. The third contribution is the improvement of the RCB algorithm in combination with the hypothesis testing method, and the RCS (Rank-Correlation-Statistics) algorithm is proposed to solve the theoretical basis for the threshold selection. Compared with the existing technology on 7 networks, the RCS algorithm is superior to existing algorithms in terms of both accuracy and time performance. In addition, simulations show that the RCS algorithm can achieve a good time performance and accuracy on low-dimensional large samples, high-dimensional large samples and real data.

为了研究因果发现中加性噪声模型的结构学习，结合Rank Correlation的方法提出了一种新的算法，即RCB（Rank-Correlation-Based）。该算法可以有效处理多元线性高斯、非高斯和多元非线性非高斯数据。在本文中，我们做出了三个贡献。首先，证明了秩相关可以作为独立性检验的标准。其次，通过一系列的实验，找到秩相关的最优阈值来选择目标节点的潜在邻居。因此，RCB 算法极大地减少了搜索空间并获得了良好的时间性能。第三个贡献是结合假设检验方法对RCB算法的改进，提出了RCS（Rank-Correlation-Statistics）算法来解决阈值选择的理论基础。与现有技术在7个网络上相比，RCS算法在精度和时间性能上均优于现有算法。此外，仿真表明，RCS算法在低维大样本、高维大样本和真实数据上均能取得良好的时间性能和精度。