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Root Cause Analysis (RCA) plays an indispensable role in distributed datasystem maintenance and operations, as it bridges the gap between faultdetection and system recovery. Existing works mainly study multidimensionallocalization or graph-based root cause localization. This paper opens up thepossibilities of exploiting the recently developed framework of explainable AI(XAI) for the purpose of RCA. In particular, we propose BALANCE (BAyesianLinear AttributioN for root CausE localization), which formulates the problemof RCA through the lens of attribution in XAI and seeks to explain theanomalies in the target KPIs by the behavior of the candidate root causes.BALANCE consists of three innovative components. First, we propose a Bayesianmulticollinear feature selection (BMFS) model to predict the target KPIs giventhe candidate root causes in a forward manner while promoting sparsity andconcurrently paying attention to the correlation between the candidate rootcauses. Second, we introduce attribution analysis to compute the attributionscore for each candidate in a backward manner. Third, we merge the estimatedroot causes related to each KPI if there are multiple KPIs. We extensivelyevaluate the proposed BALANCE method on one synthesis dataset as well as threereal-world RCA tasks, that is, bad SQL localization, container faultlocalization, and fault type diagnosis for Exathlon. Results show that BALANCEoutperforms the state-of-the-art (SOTA) methods in terms of accuracy with theleast amount of running time, and achieves at least $6\%$ notably higheraccuracy than SOTA methods for real tasks. BALANCE has been deployed toproduction to tackle real-world RCA problems, and the online results furtheradvocate its usage for real-time diagnosis in distributed data systems.

BALANCE: Bayesian Linear Attribution for Root Cause Localization