Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

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, 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 time from a credit officer. Thus, it is not possible to trigger this intervention in all cases. This paper proposes a prescriptive monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints. The 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 a case’s outcome. These estimates are used to allocate resources to interventions to maximize a cost function. A preliminary evaluation suggests that the approach produces a higher net gain than a purely predictive (non-causal) baseline.