An Approach to Highdimensional Data Causality Inference

Zhang Hao

doi:10.3969/j.issn.1007-7162.2015.01.024

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An Approach to Highdimensional Data Causality Inference

更新时间：2026-05-27

- An Approach to Highdimensional Data Causality Inference
- Vol. 32, Issue 1, Pages: 117-120(2015)
- 作者机构：
- 作者简介：
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- DOI：10.3969/j.issn.1007-7162.2015.01.024
  CLC：
- Online First：05 March 2015，
  
  Published：2015
- 稿件说明：
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张浩. 一种高维数据的因果推断算法[J]. 广东工业大学学报, 2015, 32(1): 117-120.

Zhang Hao. An Approach to Highdimensional Data Causality Inference[J]. 2015, 32(1): 117-120.
张浩. 一种高维数据的因果推断算法[J]. 广东工业大学学报, 2015, 32(1): 117-120. DOI： 10.3969/j.issn.1007-7162.2015.01.024.

Zhang Hao. An Approach to Highdimensional Data Causality Inference[J]. 2015, 32(1): 117-120. DOI： 10.3969/j.issn.1007-7162.2015.01.024.

摘要

发现数据间的因果关系是科学领域的一个重要问题，然而在高维数据中的因果推断暂时还没有有效的算法. 这里提出了一种基于条件独立性测试和互信息的适应于高维数据的因果推断算法. 该算法先用条件独立性测试和互信息降低数据集的维度，然后利用一种基于非线性加噪声模型的算法对节点间的方向进行判别. 数据试验表明，该算法在高维数据的情况下要优于目前其他的算法.

Abstract

Discovering causality from the data set is one of the basic problems in the scientific field. However

there is still no effective algorithm to discover causalities from highdimensional data. We present an algorithm for large scale causality discovery

which is based on conditional independence test and mutual information. Firstly

the algorithm reduces the dimensionality of data set by conditional independence test and mutual information

and then we employ an approach based on nonlinear additive noise models to distinguish the directions between nodes. Experimental results show that it outperforms other methods.

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