ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods

• URL: http://arxiv.org/abs/2509.00797v1
• Date: Sun, 31 Aug 2025 10:54:43 GMT
• Title: ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
• Authors: Jakob De Moor, Hans Weytjens, Johannes De Smedt,
• Abstract summary: Prescriptive Process Monitoring (PresPM) focuses on optimizing processes through real-time interventions based on event log data.<n> evaluating PresPM methods is challenging due to the lack of ground-truth outcomes for all intervention actions in datasets.<n>We introduce ProCause, a generative approach that supports both sequential and non-sequential models.
• Score: 2.4010681808413397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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