analiza aiqbench
SCOPE addresses a critical gap in Prescriptive Process Monitoring by enabling aligned sequential interventions using causal learners on observational data, thereby avoiding the reality gaps and biases associated with simulation-based reinforcement learning. The method employs backward induction to propagate intervention impacts from final to initial decision points, demonstrating consistent KPI optimization over state-of-the-art techniques in both synthetic and semi-synthetic benchmarks. This approach offers a robust, data-efficient framework for organizations seeking to manage complex, interdependent business process workflows without relying on artificial process approximations.
SCOPE: Sequential Causal Optimization of Process Interventions
arXiv:2512.17629v4 Announce Type: replace Abstract: Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KPIs). In realistic settings, interventions are rarely isolated: organizations need to align sequences of interventions to jointly steer the outcome of a case. Existing PresPM approaches only partially address this challenge. Many focus on a single intervention decision, while others treat multiple interventions independently, ignoring how they interact over time. Methods that do address these dependencies depend either on simulation or data augmentation to approximate the process to train a Reinforcement Learning (RL) agent, which may create a reality gap and introduce bias. We introduce SCOPE (Sequential Causal Optimization of Process Interventions), a PresPM approach that learns aligned sequential intervention recommendations. SCOPE employs backward induction to estimate the effect of each candidate intervention action, propagating its impact from the final decision point back to the first. By leveraging causal learners, our method can utilize observational data directly, unlike methods that require constructing process approximations for RL. Experiments on both an existing synthetic dataset and a new semi-synthetic dataset show that SCOPE consistently outperforms state-of-the-art PresPM techniques in optimizing the KPI. The novel semi-synthetic setup, based on a real-life event log, is provided as a reusable benchmark for future work on sequential PresPM.
Abstract:Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KPIs). In realistic settings, interventions are rarely isolated: organizations need to align sequences of interventions to jointly steer the outcome of a case. Existing PresPM approaches only partially address this challenge. Many focus on a single intervention decision, while others treat multiple interventions independently, ignoring how they interact over time. Methods that do address these dependencies depend either on simulation or data augmentation to approximate the process to train a Reinforcement Learning (RL) agent, which may create a reality gap and introduce bias. We introduce SCOPE (Sequential Causal Optimization of Process Interventions), a PresPM approach that learns aligned sequential intervention recommendations. SCOPE employs backward induction to estimate the effect of each candidate intervention action, propagating its impact from the final decision point back to the first. By leveraging causal learners, our method can utilize observational data directly, unlike methods that require constructing process approximations for RL. Experiments on both an existing synthetic dataset and a new semi-synthetic dataset show that SCOPE consistently outperforms state-of-the-art PresPM techniques in optimizing the KPI. The novel semi-synthetic setup, based on a real-life event log, is provided as a reusable benchmark for future work on sequential PresPM.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2512.17629 [cs.LG]
(or arXiv:2512.17629v4 [cs.LG] for this version)
Text integral sub licența deschisă a publicației · 221 cuvinte
Subiecte conexe
Task Vectors Can Be Linearly Decomposed into Interpretable and Controllable Components
arXiv · 2 nov. 2026
PASTABench: Proactive Assessment of Sequential Trajectories for Agent Safety
arXiv · 24 sept. 2026
Causal Bayesian Optimization: Foundations, Methods, and Applications
arXiv · 22 sept. 2026
Minimally Invasive Steering of Language Models
arXiv · 26 sept. 2026