Title: Simple Root Cause Analysis by Separable Likelihoods,
Abstract: 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).