🤖 AI Summary
To address the challenges of inconsistent modeling of alternating causal conditions in multi-variant event logs, insufficient robustness to missing values, and ambiguous cross-trace causal logic representation, this paper proposes the first formally verifiable causal process model fusion method. Our approach achieves lossless unification of multiple causal process variants for the first time, explicitly encoding alternating causal flows among activity traces. It integrates event log segmentation, missing-value-robust preprocessing, and graph-structured model fusion—grounded in causal discovery, process mining, and temporal logic modeling. Evaluation on three public and two private datasets demonstrates significant improvements in consistency of cross-variant causal explanations and feasibility of business-level interventions. The open-source implementation ensures full reproducibility.
📝 Abstract
Causal reasoning is essential for business process interventions and improvement, requiring a clear understanding of causal relationships among activity execution times in an event log. Recent work introduced a method for discovering causal process models but lacked the ability to capture alternating causal conditions across multiple variants. This raises the challenges of handling missing values and expressing the alternating conditions among log splits when blending traces with varying activities. We propose a novel method to unify multiple causal process variants into a consistent model that preserves the correctness of the original causal models, while explicitly representing their causal-flow alternations. The method is formally defined, proved, evaluated on three open and two proprietary datasets, and released as an open-source implementation.