论文标题
因果网络学习，具有不可逆转的功能关系
Causal network learning with non-invertible functional relationships
论文作者
论文摘要
从观察数据发现因果关系是许多领域的重要问题。最近的几个结果已经建立了具有非高斯和/或非线性结构方程模型（SEMS）的因果关系的可识别性。在本文中，我们专注于由非可逆函数定义的非线性SEM，这些函数存在于许多数据域中，并提出了针对不可粘的双变量因果模型的新型测试。我们进一步开发了一种将该测试纳入包含线性和非线性因果关系的DAG结构的方法。通过广泛的数值比较，我们表明我们的算法在识别因果图形结构中的表现优于现有的DAG学习方法。我们说明了我们方法在学习因果网络中的实际应用，用于从CHIP-SEQ数据中转录因子的结合结合。
Discovery of causal relationships from observational data is an important problem in many areas. Several recent results have established the identifiability of causal DAGs with non-Gaussian and/or nonlinear structural equation models (SEMs). In this paper, we focus on nonlinear SEMs defined by non-invertible functions, which exist in many data domains, and propose a novel test for non-invertible bivariate causal models. We further develop a method to incorporate this test in structure learning of DAGs that contain both linear and nonlinear causal relations. By extensive numerical comparisons, we show that our algorithms outperform existing DAG learning methods in identifying causal graphical structures. We illustrate the practical application of our method in learning causal networks for combinatorial binding of transcription factors from ChIP-Seq data.
