Root Cause Analysis of Outliers in Unknown Cyclic Graphs
This addresses the challenge of outlier analysis in unknown cyclic systems, offering a method for root cause identification, though it appears incremental as it builds on existing causal graph frameworks.
The paper tackles the problem of identifying root causes of outliers in cyclic causal graphs with linear structural equations, showing that a short list of potential root causes can be identified without prior knowledge of the graph, provided the perturbation is strong and propagates consistently.
We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible to identify a short list of potential root causes provided that the perturbation is sufficiently strong and propagates according to the same structural equations as in the normal mode. This shortlist consists of the true root causes together with those of its parents lying on a cycle with the root cause. Notably, our method does not require prior knowledge of the causal graph.