SCOPE: Sequential Causal Optimization of Process Interventions

Researchers introduce SCOPE, a new machine learning approach for Prescriptive Process Monitoring that optimizes sequential business interventions using causal inference rather than simulation-based reinforcement learning. The method addresses a critical gap in existing systems by accounting for how multiple interventions interact over time while working directly with observational data, demonstrated through testing on synthetic and semi-synthetic datasets.

SCOPE represents a meaningful advancement in prescriptive analytics, addressing a fundamental limitation in how organizations optimize business processes. Traditional Prescriptive Process Monitoring systems struggle with sequential decision-making because they either treat interventions as isolated events or rely on simulation and data augmentation techniques that introduce approximation errors and potential bias. This research tackles that problem by employing backward induction combined with causal learning frameworks, allowing the system to estimate intervention effects by propagating impacts backward through the decision sequence.

The approach gains practical importance because most real-world business operations require coordinated interventions rather than single actions. A supply chain manager, for instance, might need to approve purchase orders, adjust inventory allocations, and reroute shipments in a specific sequence—each decision affecting the success of subsequent ones. Previous RL-based methods struggled because they required constructing synthetic process models for training, creating a reality gap between simulated and actual outcomes.

SCOPE's direct use of observational data without process approximation addresses this efficiency concern directly. The research also contributes a semi-synthetic benchmark dataset derived from real event logs, providing a reproducible testing standard for future work. For organizations managing complex processes across manufacturing, healthcare, finance, or logistics, such advances translate to better KPI outcomes and operational efficiency.

The framework's scalability and performance gains over state-of-the-art methods suggest adoption potential in enterprise process mining platforms. Future development should focus on real-world validation across diverse industry verticals and exploration of how the approach handles non-linear intervention interactions.

• →SCOPE uses backward induction and causal learning to recommend sequential interventions without requiring simulation or data augmentation
• →The method directly utilizes observational data, reducing the reality gap found in reinforcement learning-based approaches
• →Experiments demonstrate consistent performance improvements over existing Prescriptive Process Monitoring techniques
• →A new semi-synthetic benchmark dataset based on real event logs provides a reusable standard for future sequential PresPM research
• →The approach addresses practical business needs where interventions must be coordinated temporally rather than treated independently