An experimental comparison of linear non-Gaussian causal discovery methods and their variants
An experimental comparison of linear non-Gaussian causal discovery methods and their variants
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DOI：
10.1109/ijcnn.2010.5596737
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
Yasuhiro Sogawa;Shohei Shimizu;Y. Kawahara;T. Washio
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文献类型：
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作者：
Yasuhiro Sogawa;Shohei Shimizu;Y. Kawahara;T. Washio
已经提出了许多基于高斯性的多变量技术来识别观测变量的因果网络。这些方法有几个问题，以至于它们在没有任何先验知识的情况下无法唯一地识别因果网络。为了缓解这一问题，提出了一种基于非高斯性的身份识别方法LINAM。虽然Lingam可能在不使用任何先验知识的情况下识别唯一的因果网络，但它需要适当地检查因果网络的独立性假设，并仅使用有限的观测数据点来搜索正确的因果网络。另一方面，最近提出了一种基于核的独立性度量，它更严格地评估了独立性。此外，包括波束搜索在内的一些先进的通用搜索算法在过去也得到了广泛的研究。在本文中，我们提出了Lingam方法的一些变体，其中引入了基于核的方法和波束搜索，使得因果网络识别更加准确。此外，我们在识别的准确性和稳健性方面对Lingam及其变体进行了实验表征。
Many multivariate Gaussianity-based techniques for identifying causal networks of observed variables have been proposed. These methods have several problems such that they cannot uniquely identify the causal networks without any prior knowledge. To alleviate this problem, a non-Gaussianity-based identification method LiNGAM was proposed. Though the LiNGAM potentially identifies a unique causal network without using any prior knowledge, it needs to properly examine independence assumptions of the causal network and search the correct causal network by using finite observed data points only. On another front, a kernel based independence measure that evaluates the independence more strictly was recently proposed. In addition, some advanced generic search algorithms including beam search have been extensively studied in the past. In this paper, we propose some variants of the LiNGAM method which introduce the kernel based method and the beam search enabling more accurate causal network identification. Furthermore, we experimentally characterize the LiNGAM and its variants in terms of accuracy and robustness of their identification.