The WHY in Business Processes: Unification of Causal Process Models

2025/05/28 by Yuval David, Fabiana Fournier, David, Yuval +5
Business, Management and Accounting · Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Business Process Modeling and Analysis #FOS: Computer and information sciences #Software System Performance and Reliability

paper · pdf · doi:10.48550/arxiv.2505.22871

openalex publication_date 2025/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• An Explainable Decision Support System for Predictive Process Analytics
• When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints
• Process Mining Meets Causal Machine Learning: Discovering Causal Rules\n from Event Logs
• A Survey on Causal Inference
• Review of inverse probability weighting for dealing with missing data

Cited by

• Agentic AI Process Observability: Discovering Behavioral Variability

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