计算机工程

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基于级联加性噪声模型的因果结构学习算法

  

  • 发布日期:2021-01-12

Causal Structure Learning Algorithm with Cascade Nonlinear Additive Noise Model

  • Published:2021-01-12

摘要: 面向观察数据的因果结构学习是很多学科的基本问题。在局部观测的现实应用场景中,隐变量的存在对现有基于约 束的方法和基于因果函数的方法带来众多挑战。虽然现有级联非线性加性噪声模型可以解决隐藏中间变量场景的因果方向推 断问题,然而该方法并不适用于因果网络学习。为此,我们提出了一种基于级联非线性加性噪声模型的因果结构学习算法。 在该算法中,推广了级联非线性加性噪声模型在多个因果隐变量下的适用性,进而提出了两阶段因果结构学习方法。首先, 进行因果骨架学习,然后基于级联非线性加性噪声模型推断因果方向。实验结果表明本文提出方法在虚拟结构和真实结构下 具有更高的准确度。

Abstract: 】 】Learning causal structure from observational data is a basic problem in many disciplines. In the real application scenarios, the existence of hidden variables brings many challenges to the existing constraint-based methods and functional-based causal methods. Although the existing cascaded nonlinear additive noise model can discover the causal direction in the presence of the hidden intermediate variables, this method is not suitable for causal structure learning. For this reason, we propose a mixed causal structure learning algorithm based on cascaded nonlinear additive noise model. In this algorithm, we extend the cascaded nonlinear additive noise model under multiple causal hidden variables. Then a two-stage causal structure learning framework is proposed. The first stage is to learn the causal skeleton, and the second stage is to infer the direction of the causal pair based on the cascaded nonlinear additive noise model. The experimental results show that the proposed method has higher accuracy on both synthetic and real-world structures.