Causal network learning with non-invertible functional relationships
Causal network learning with non-invertible functional relationships
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DOI：
10.1016/j.csda.2020.107141
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Bingling Wang;Qing Zhou
中科院分区：
文献类型：
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作者：
Bingling Wang;Qing Zhou
从观测数据中发现因果关系是许多领域的重要问题。最近的一些结果已经建立了因果有向无环图（DAG）与非高斯和/或非线性结构方程模型（SEM）的可识别性。针对存在于许多数据域中的由不可逆函数定义的非线性SEM，提出了一种新的检验不可逆二元因果模型的方法。算法进一步发展，将此测试包含线性和非线性因果关系的DAG的结构学习。大量的数值比较表明，所提出的算法优于现有的DAG学习方法在识别因果图形结构。通过学习因果网络的组合结合的转录因子从ChIP-Seq数据的方法的实际应用进行说明。
Discovery of causal relationships from observational data is an important problem in many areas. Several recent results have established the identifiability of causal directed acyclic graphs (DAGs) with non-Gaussian and/or nonlinear structural equation models (SEMs). Focusing on nonlinear SEMs defined by non-invertible functions, which exist in many data domains, a novel test is proposed for non-invertible bivariate causal models. Algorithms are further developed to incorporate this test in structure learning of DAGs that contain both linear and nonlinear causal relations. Extensive numerical comparisons show that the proposed algorithms outperform existing DAG learning methods in identifying causal graphical structures. The practical application of the methods is illustrated by learning causal networks for combinatorial binding of transcription factors from ChIP-Seq data.