  In this paper, we propose REASON, a novel framework that enables the
automatic discovery of both intra-level (i.e., within-network) and inter-level
(i.e., across-network) causal relationships for root cause localization. REASON
consists of Topological Causal Discovery and Individual Causal Discovery. The
Topological Causal Discovery component aims to model the fault propagation in
order to trace back to the root causes. To achieve this, we propose novel
hierarchical graph neural networks to construct interdependent causal networks
by modeling both intra-level and inter-level non-linear causal relations. Based
on the learned interdependent causal networks, we then leverage random walks
with restarts to model the network propagation of a system fault. The
Individual Causal Discovery component focuses on capturing abrupt change
patterns of a single system entity. This component examines the temporal
patterns of each entity's metric data (i.e., time series), and estimates its
likelihood of being a root cause based on the Extreme Value theory. Combining
the topological and individual causal scores, the top K system entities are
identified as root causes. Extensive experiments on three real-world datasets
with case studies demonstrate the effectiveness and superiority of the proposed
framework.
