2019/12/05 by Dominik Janzing, Kailash Budhathoki, Janzing, Dominik +5 · 5 citations
Computer Science · Decision Sciences · #Anomaly Detection Techniques and Applications #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Risk and Safety Analysis #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1912.02724
openalex publication_date 2019/12/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We describe a formal approach to identify 'root causes' of outliers observed in n variables X1,…,Xn 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.