  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.
