New Permutation Algorithms for Causal Discovery Using ICA
New Permutation Algorithms for Causal Discovery Using ICA
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DOI：
10.1007/11679363_15
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发表时间：
2006-03
期刊：
影响因子：
12.8
通讯作者：
P. Hoyer;Shohei Shimizu;Aapo Hyvärinen;Y. Kano;Antti J. Kerminen
中科院分区：
文献类型：
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作者：
P. Hoyer;Shohei Shimizu;Aapo Hyvärinen;Y. Kano;Antti J. Kerminen
因果发现是从统计数据[1，2]中找到可信的因果关系的任务。这些方法依赖于关于数据生成过程的各种假设，以从不受控制的观察中识别数据。我们最近提出了一种基于独立分量分析(ICA)的因果发现方法LINAM[3]，展示了如何在线性、非高斯性和没有隐藏变量的假设下完全识别数据生成过程。在本文中，在简要总结了这种方法之后，我们重点讨论了当所考虑的变量数量较大时所遇到的算法问题。因此，我们将该方法的适用范围扩展到含有数十个或更多变量的数据集。实验证实了提出的算法的性能，该算法是作为我们免费提供的最新版本的MatLab/Octave Lingam包的一部分实现的。
Causal discovery is the task of finding plausible causal relationships from statistical data [1, 2]. Such methods rely on various assumptions about the data generating process to identify it from uncontrolled observations. We have recently proposed a causal discovery method based on independent component analysis (ICA) called LiNGAM [3], showing how to completely identify the data generating process under the assumptions of linearity, non-gaussianity, and no hidden variables. In this paper, after briefly recapitulating this approach, we focus on the algorithmic problems encountered when the number of variables considered is large. Thus we extend the applicability of the method to data sets with tens of variables or more. Experiments confirm the performance of the proposed algorithms, implemented as part of the latest version of our freely available Matlab/Octave LiNGAM package.