A Linear Non-Gaussian Acyclic Model for Causal Discovery
A Linear Non-Gaussian Acyclic Model for Causal Discovery
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发表时间：
2006-12
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通讯作者：
Shohei Shimizu;P. Hoyer;Aapo Hyvärinen;Antti J. Kerminen
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作者：
Shohei Shimizu;P. Hoyer;Aapo Hyvärinen;Antti J. Kerminen
近年来，人们提出了几种从非实验数据中发现因果结构的方法。这些方法对数据生成过程做出各种假设，以便于从纯观测数据中识别。继续这条线的研究，我们展示了如何发现完整的因果结构的连续值数据，假设（a）的数据生成过程是线性的，（B）有没有未观察到的混杂因素，（c）干扰变量有非高斯分布的非零方差。该解决方案依赖于使用被称为独立成分分析的统计方法，并且不需要任何预先指定的变量时间排序。我们提供了一个完整的Matlab软件包来执行这种LiNGAM分析（线性非高斯非循环模型的缩写），并使用人工生成的数据和真实世界的数据证明了该方法的有效性。
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data. Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to discover the complete causal structure of continuous-valued data, under the assumptions that (a) the data generating process is linear, (b) there are no unobserved confounders, and (c) disturbance variables have non-Gaussian distributions of non-zero variances. The solution relies on the use of the statistical method known as independent component analysis, and does not require any pre-specified time-ordering of the variables. We provide a complete Matlab package for performing this LiNGAM analysis (short for Linear Non-Gaussian Acyclic Model), and demonstrate the effectiveness of the method using artificially generated data and real-world data.