SCOPE: Sequential Causal Optimization of Process Interventions

TL;DR

SCOPE is a novel prescriptive process monitoring method that optimizes sequences of interventions by estimating their causal effects through backward induction, directly utilizing observational data to improve KPI outcomes.

Contribution

It introduces a sequential intervention recommendation approach using causal learners and backward induction, addressing limitations of existing methods that ignore intervention dependencies.

Findings

SCOPE outperforms state-of-the-art PresPM techniques in KPI optimization.

The method effectively utilizes observational data without process simulation.

A new semi-synthetic benchmark based on real event logs is provided.

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.