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Identifying root causes of anomalies in causal processes is vital acrossdisciplines. Once identified, one can isolate the root causes and implementnecessary measures to restore the normal operation. Causal processes are oftenmodelled as graphs with entities being nodes and their paths/interconnectionsas edge. Existing work only consider the contribution of nodes in thegenerative process, thus can not attribute the outlier score to the edges ofthe mechanism if the anomaly occurs in the connections. In this paper, weconsider both individual edge and node of each mechanism when identifying theroot causes. We introduce a noisy functional causal model to account for thispurpose. Then, we employ Bayesian learning and inference methods to infer thenoises of the nodes and edges. We then represent the functional form of atarget outlier leaf as a function of the node and edge noises. Finally, wepropose an efficient gradient-based attribution method to compute the anomalyattribution scores which scales linearly with the number of nodes and edges.Experiments on simulated datasets and two real-world scenario datasets showbetter anomaly attribution performance of the proposed method compared to thebaselines. Our method scales to larger graphs with more nodes and edges.

Root Cause Explanation of Outliers under Noisy Mechanisms