Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

TL;DR

This paper introduces a resource-aware prescriptive process monitoring method that uses causal inference and predictive modeling to optimize interventions under resource constraints, improving net gains in business processes.

Contribution

It presents a novel approach combining causal inference with predictive modeling to allocate interventions efficiently within resource limits.

Findings

Higher net gain compared to baseline methods

Effective resource allocation for interventions

Improved process performance

Abstract

Prescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, specific interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant's chances of taking a loan. This intervention requires a certain amount of time from a credit officer, and thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The proposed technique relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on the outcome of the case. These outputs are then used to allocate resources to interventions to maximize a cost function. A preliminary empirical evaluation suggests that the proposed approach produces a higher net gain than a purely predictive (non-causal) baseline.