ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915472541220864 |
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| author | De Moor, Jakob Weytjens, Hans De Smedt, Johannes |
| author_facet | De Moor, Jakob Weytjens, Hans De Smedt, Johannes |
| contents | Prescriptive Process Monitoring (PresPM) is the subfield of Process Mining that focuses on optimizing processes through real-time interventions based on event log data. Evaluating PresPM methods is challenging due to the lack of ground-truth outcomes for all intervention actions in datasets. A generative deep learning approach from the field of Causal Inference (CI), RealCause, has been commonly used to estimate the outcomes for proposed intervention actions to evaluate a new policy. However, RealCause overlooks the temporal dependencies in process data, and relies on a single CI model architecture, TARNet, limiting its effectiveness. To address both shortcomings, we introduce ProCause, a generative approach that supports both sequential (e.g., LSTMs) and non-sequential models while integrating multiple CI architectures (S-Learner, T-Learner, TARNet, and an ensemble). Our research using a simulator with known ground truths reveals that TARNet is not always the best choice; instead, an ensemble of models offers more consistent reliability, and leveraging LSTMs shows potential for improved evaluations when temporal dependencies are present. We further validate ProCause's practical effectiveness through a real-world data analysis, ensuring a more reliable evaluation of PresPM methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods De Moor, Jakob Weytjens, Hans De Smedt, Johannes Machine Learning Artificial Intelligence Methodology Prescriptive Process Monitoring (PresPM) is the subfield of Process Mining that focuses on optimizing processes through real-time interventions based on event log data. Evaluating PresPM methods is challenging due to the lack of ground-truth outcomes for all intervention actions in datasets. A generative deep learning approach from the field of Causal Inference (CI), RealCause, has been commonly used to estimate the outcomes for proposed intervention actions to evaluate a new policy. However, RealCause overlooks the temporal dependencies in process data, and relies on a single CI model architecture, TARNet, limiting its effectiveness. To address both shortcomings, we introduce ProCause, a generative approach that supports both sequential (e.g., LSTMs) and non-sequential models while integrating multiple CI architectures (S-Learner, T-Learner, TARNet, and an ensemble). Our research using a simulator with known ground truths reveals that TARNet is not always the best choice; instead, an ensemble of models offers more consistent reliability, and leveraging LSTMs shows potential for improved evaluations when temporal dependencies are present. We further validate ProCause's practical effectiveness through a real-world data analysis, ensuring a more reliable evaluation of PresPM methods. |
| title | ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2509.00797 |