Root Cause Explanation of Outliers under Noisy Mechanisms
This work addresses a specific bottleneck in anomaly attribution for causal processes, offering an incremental improvement over existing methods.
The paper tackles the problem of identifying root causes of anomalies in causal graphs by considering both nodes and edges, whereas existing methods only consider nodes. The proposed method uses Bayesian learning and gradient-based attribution to compute anomaly scores, showing better performance on simulated and real-world datasets while scaling linearly with graph size.
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.