Data-driven root cause analysis via causal discovery using time-to-event data

Abstract

Data-driven approaches to root cause analysis (RCA) have received at- tention recently due to their ability to exploit increasing data availability for more effective root cause identification in complex processes. Advancements in the field of causal inference enable unbiased causal investigations using observational data. This study proposes a data-driven RCA method and a time-to-event (TTE) data simulation procedure built on the structural causal model (SCM) framework. A novel causality-based method is introduced for learning a representation of root cause mechanisms, termed in this work as root cause graphs (RCGs), from observational TTE data. Three case scenar- ios are used to generate TTE datasets for evaluating the proposed method. Finally, the utility of the method is demonstrated by using recovered RCGs to guide the estimation of root cause treatment effects. In the presence of me- diation, RCG-guided models produce superior estimates of root cause total effects compared to models that adjust for all covariates.