标题： 学习包含高斯噪声的线性有向无环因果模型的祖先关系 显示英文标题

标题： Learning linear acyclic causal model including Gaussian noise using ancestral relationships

摘要： 本文讨论了学习因果有向无环图（DAG）的算法。PC算法除了假设忠实性外没有其他假设，并且只能识别到马尔可夫等价类。LiNGAM假设因果模型具有线性和连续非高斯扰动，定义LiNGAM的因果DAG被证明是完全可辨识的。PC-LiNGAM，即PC算法和LiNGAM的混合方法，即使存在高斯扰动，也能识别线性因果模型的分布等价模式。然而，在最坏情况下，PC-LiNGAM的时间复杂度对于变量数量是阶乘级的。在本文中，我们提出了一种算法，用于以比PC-LiNGAM更低的时间复杂度学习线性因果模型的分布等价模式，该算法利用了Maeda和Shimizu提出的因果祖先寻找算法，并将其推广以考虑高斯扰动。 显示英文摘要

摘要： This paper discusses algorithms for learning causal DAGs. The PC algorithm makes no assumptions other than the faithfulness to the causal model and can identify only up to the Markov equivalence class. LiNGAM assumes linearity and continuous non-Gaussian disturbances for the causal model, and the causal DAG defining LiNGAM is shown to be fully identifiable. The PC-LiNGAM, a hybrid of the PC algorithm and LiNGAM, can identify up to the distribution-equivalence pattern of a linear causal model, even in the presence of Gaussian disturbances. However, in the worst case, the PC-LiNGAM has factorial time complexity for the number of variables. In this paper, we propose an algorithm for learning the distribution-equivalence patterns of a linear causal model with a lower time complexity than PC-LiNGAM, using the causal ancestor finding algorithm in Maeda and Shimizu, which is generalized to account for Gaussian disturbances.