SCOPE: Sequential Causal Optimization of Process Interventions

2025-12-19

AI Summary

Existing PresPM methods struggle to model temporal dependencies and causal effects of multi-stage interventions, either restricting decisions to single-step actions or relying on simulation/data augmentation—introducing a reality gap. This paper proposes the first backward-induction-based causal effect propagation framework that directly learns KPI-driven sequential intervention policies from observational event logs, eliminating the need for environment simulation. Our method integrates doubly robust estimation, propensity score weighting, and dynamic-programming-style backward induction to explicitly propagate and jointly optimize causal effects across interventions. Evaluated on synthetic and novel semi-synthetic real-world benchmarks, it significantly outperforms state-of-the-art methods, achieving 12.7%–23.4% KPI improvement. We further release a reproducible evaluation benchmark.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Prescriptive Process Monitoring (PresPM) recommends interventions during 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 fall short in this respect. 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 can create a reality gap and introduce bias. We introduce SCOPE, 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 reinforcement learning. 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.

Problem

• Optimizes sequential interventions in business processes
• Addresses dependencies between multiple interventions over time
• Uses causal learners with observational data directly

Innovation

• Uses backward induction for sequential intervention optimization
• Employs causal learners to directly utilize observational data
• Avoids process approximations required by reinforcement learning methods