School of Computer Science, Guangdong University of Technology, Guangzhou, China.
School of Computer Science, Guangdong University of Technology, Guangzhou, China.
Neural Netw. 2020 Apr;124:193-201. doi: 10.1016/j.neunet.2020.01.006. Epub 2020 Jan 20.
Identification of the causal direction is a fundamental problem in many scientific research areas. The independence between the noise and the cause variable is a widely used assumption to identify the causal direction. However, such an independence assumption is usually violated due to heteroscedasticity of the real-world data. In this paper, we propose a new criterion for the causal direction identification which is robust to the heteroscedasticity of the data. In detail, the fourth-order moment of noise is proposed to measure the asymmetry between the cause and effect. A heteroscedastic Gaussian process regression-based estimation of the fourth-order moment is proposed accordingly. Under some commonly used assumptions of the causal mechanism, we theoretically show that the noise's fourth-order moment of the causal direction is smaller than that of the anti-causal direction. Experimental results on both simulated and real-world data illustrate the efficiency of the proposed approach.
因果方向的识别是许多科学研究领域的一个基本问题。噪声与原因变量之间的独立性是用于识别因果方向的一个广泛应用的假设。然而，由于实际数据的异方差性，这种独立性假设通常会被违反。在本文中，我们提出了一种新的因果方向识别准则，该准则对数据的异方差性具有鲁棒性。具体来说，我们提出用噪声的四阶矩来衡量因果之间的不对称性。相应地，提出了基于异方差高斯过程回归的四阶矩估计。在因果机制的一些常用假设下，我们从理论上证明了因果方向噪声的四阶矩小于反因果方向噪声的四阶矩。在模拟数据和真实世界数据上的实验结果表明了所提出方法的有效性。
