Root Cause Explanation of Outliers under Noisy Mechanisms
Phuoc Nguyen Truyen Tran Sunil Gupta Thin Nguyen Svetha Venkatesh
Identifying root causes of anomalies in causal processes is vital across
disciplines. Once identified, one can isolate the root causes and implement
necessary measures to restore the normal operation. Causal processes are often
modelled as graphs with entities being nodes and their paths/interconnections
as edge. Existing work only consider the contribution of nodes in the
generative process, thus can not attribute the outlier score to the edges of
the mechanism if the anomaly occurs in the connections. In this paper, we
consider both individual edge and node of each mechanism when identifying the
root causes. We introduce a noisy functional causal model to account for this
purpose. Then, we employ Bayesian learning and inference methods to infer the
noises of the nodes and edges. We then represent the functional form of a
target outlier leaf as a function of the node and edge noises. Finally, we
propose an efficient gradient-based attribution method to compute the anomaly
attribution scores which scales linearly with the number of nodes and edges.
Experiments on simulated datasets and two real-world scenario datasets show
better anomaly attribution performance of the proposed method compared to the
baselines. Our method scales to larger graphs with more nodes and edges.