Causal Alignment Based Fault Root Causes Localization for Wireless Network

Abstract

Localizing fault root causes is challenging but critical for wireless network operation and maintenance. Though supervised methods have shown promising results in training samples, most of the existing approaches assume that the training and the testing samples are independent and identical distributed. Such an i.i.d assumption usually does not hold due to network faults that may occur in different devices across different domains (well known as the distribution shift). Thus, it is necessary to align distributions between the training and test data set. Motivated by the stability of the causal mechanism across the domains, a Causal Alignment based Root Cause Localization (CARCL) framework, including the causal alignment and the multi-stage classifier, is proposed. CARCL first offers to align the distributions locally for each causal module but not globally on the complete variable set. We further develop a multi-stage classifier to determine the root causes with the help of predicted pseudo labels. The experiments demonstrate a superior performance of our method.