Causal structure based root cause analysis of outliers
This provides a causal framework for root cause analysis in anomaly detection, which is incremental as it builds on existing causal and game theory concepts.
The paper tackles the problem of identifying root causes of outliers in multivariate data with known causal structure by introducing conditional outlier scores and using Shapley values to attribute anomalies to ancestors in the DAG, resulting in a formal quantification method.
We describe a formal approach to identify 'root causes' of outliers observed in $n$ variables $X_1,\dots,X_n$ in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG). To this end, we first introduce a systematic way to define outlier scores. Further, we introduce the concept of 'conditional outlier score' which measures whether a value of some variable is unexpected *given the value of its parents* in the DAG, if one were to assume that the causal structure and the corresponding conditional distributions are also valid for the anomaly. Finally, we quantify to what extent the high outlier score of some target variable can be attributed to outliers of its ancestors. This quantification is defined via Shapley values from cooperative game theory.