The WHY in Business Processes: Unification of Causal Process Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908383379980288 |
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| author | David, Yuval Fournier, Fabiana Limonad, Lior Skarbovsky, Inna |
| author_facet | David, Yuval Fournier, Fabiana Limonad, Lior Skarbovsky, Inna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22871 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The WHY in Business Processes: Unification of Causal Process Models David, Yuval Fournier, Fabiana Limonad, Lior Skarbovsky, Inna Artificial Intelligence 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. |
| title | The WHY in Business Processes: Unification of Causal Process Models |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2505.22871 |