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

Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition