The WHY in Business Processes: Unification of Causal Process Models

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 variants.

This challenge arises from handling missing values and expressing alternating conditions among different log splits when blending traces with varying activities.

We propose a novel method to unify multiple causal process variants into a cohesive model that preserves consistency with the original causal models while explicitly representing their causal-flow alternations. The method is formally defined, evaluated on five benchmark datasets—three open and two proprietary—and released as an open-source implementation.