SCOPE: New Method for Sequential Business Process Interventions Using Causal Learning
Researchers introduced SCOPE, a new approach for recommending sequences of interventions in business processes that optimizes key performance indicators by learning how interventions interact over time. Unlike existing methods that rely on simulation or data augmentation, SCOPE uses causal learning to work directly with observational data through backward induction. The method outperformed state-of-the-art techniques in experiments and includes a new benchmark dataset for future research.
SCOPE addresses a gap in Prescriptive Process Monitoring by handling realistic scenarios where multiple interventions must be coordinated sequentially rather than in isolation. The method uses backward induction to estimate how each intervention affects outcomes, propagating impacts from the final decision point backward to earlier stages. By leveraging causal learners, SCOPE avoids the reality gap and bias issues that plague reinforcement learning approaches requiring process simulation or data augmentation. The researchers validated their approach on both an existing synthetic dataset and a new semi-synthetic dataset derived from real-world event logs. Results demonstrate consistent improvements over existing PresPM methods in optimizing key performance indicators, and the authors provide their semi-synthetic benchmark as a reusable resource for the research community.
What's missing
The paper does not discuss computational complexity or scalability limitations of the backward induction approach, nor does it address potential failure modes when causal assumptions are violated in real-world data.
What different sources said
- arXiv cs.LGCenter
SCOPE: Sequential Causal Optimization of Process Interventions
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