Simple Root Cause Analysis by Separable Likelihoods
This work addresses the need for interpretable anomaly analysis in industrial applications, though it appears incremental as it builds on existing Bayesian methods with specific assumptions.
The paper tackles the challenge of balancing accuracy and explanatory friendliness in Root Cause Analysis for anomalies by proposing a Bayesian framework with separability restrictions on the predictive posterior, validated on a real-world web server error log dataset.
Root Cause Analysis for Anomalies is challenging because of the trade-off between the accuracy and its explanatory friendliness, required for industrial applications. In this paper we propose a framework for simple and friendly RCA within the Bayesian regime under certain restrictions (that Hessian at the mode is diagonal, here referred to as \emph{separability}) imposed on the predictive posterior. We show that this assumption is satisfied for important base models, including Multinomal, Dirichlet-Multinomial and Naive Bayes. To demonstrate the usefulness of the framework, we embed it into the Bayesian Net and validate on web server error logs (real world data set).