  Fault diagnosis is critical in many domains, as faults may lead to safety
threats or economic losses. In the field of online service systems, operators
rely on enormous monitoring data to detect and mitigate failures. Quickly
recognizing a small set of root cause indicators for the underlying fault can
save much time for failure mitigation. In this paper, we formulate the root
cause analysis problem as a new causal inference task named intervention
recognition. We proposed a novel unsupervised causal inference-based method
named Causal Inference-based Root Cause Analysis (CIRCA). The core idea is a
sufficient condition for a monitoring variable to be a root cause indicator,
i.e., the change of probability distribution conditioned on the parents in the
Causal Bayesian Network (CBN). Towards the application in online service
systems, CIRCA constructs a graph among monitoring metrics based on the
knowledge of system architecture and a set of causal assumptions. The
simulation study illustrates the theoretical reliability of CIRCA. The
performance on a real-world dataset further shows that CIRCA can improve the
recall of the top-1 recommendation by 25% over the best baseline method.
