提出更高效算法，可准确推断线性因果模型的结构关系。
Learning linear acyclic causal model including Gaussian noise using ancestral relationships
- 基于祖先关系识别，改进现有方法的时间复杂度
- 在存在高斯噪声时仍能准确识别因果模式
- 适合变量较多的因果推断任务
本文研究了学习线性无环因果模型的算法。PC算法仅依赖忠实性假设，但只能识别马尔可夫等价类；LiNGAM假设线性与非高斯扰动，其因果图可完全识别；而PC-LiNGAM结合两者，可在存在高斯扰动时识别分布等价模式，但最坏情况下时间复杂度为变量数的阶乘。本文提出一种新算法，利用Maeda和Shimizu的祖先发现算法，并将其推广以处理高斯扰动，显著降低时间复杂度，同时保持对分布等价模式的准确识别能力。
原文摘要 · Abstract (English)
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.
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