Causal Explanations of Process Monitor Predictions

Abstract

Process mining is widely used to diagnose processes and identify performance and compliance issues. Specifically, Predictive Process Monitoring (PPM) techniques use AI models to predict outcomes of ongoing process instances. While these models can achieve high predictive performance, their black-box nature makes it difficult to understand the underlying reasons behind their output predictions. In this paper, we propose a novel approach for generating local (case-level) explanations of process monitor predictions based on the framework of actual causality. We define a causal model tailored to processes that captures temporal dependencies between events in a trace, thus allowing us to reason about causal influence of events on the predicted outcome. Our method uses this model implicitly to compute causes and quantify the importance of different events with respect to the predicted outcome. We present a practical, model-agnostic algorithm that approximates event responsibility given the process structure reflected in the causal model. We evaluate our approach on a range of datasets derived from real-life event logs from a standard PPM benchmark. Each dataset contains up to 130,000 traces, with trace lengths of up to 1,800 events and up to 400 distinct event types. We compare our approach with state-of-the-art local explanation methods. The results demonstrate that our approach produces more stable and concise explanations while maintaining competitive efficiency.