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In this paper, we propose REASON, a novel framework that enables theautomatic discovery of both intra-level (i.e., within-network) and inter-level(i.e., across-network) causal relationships for root cause localization. REASONconsists of Topological Causal Discovery and Individual Causal Discovery. TheTopological Causal Discovery component aims to model the fault propagation inorder to trace back to the root causes. To achieve this, we propose novelhierarchical graph neural networks to construct interdependent causal networksby modeling both intra-level and inter-level non-linear causal relations. Basedon the learned interdependent causal networks, we then leverage random walkswith restarts to model the network propagation of a system fault. TheIndividual Causal Discovery component focuses on capturing abrupt changepatterns of a single system entity. This component examines the temporalpatterns of each entity's metric data (i.e., time series), and estimates itslikelihood of being a root cause based on the Extreme Value theory. Combiningthe topological and individual causal scores, the top K system entities areidentified as root causes. Extensive experiments on three real-world datasetswith case studies demonstrate the effectiveness and superiority of the proposedframework.

Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization