  Prescriptive process monitoring approaches leverage historical data to
prescribe runtime interventions that will likely prevent negative case outcomes
or improve a process's performance. A centerpiece of a prescriptive process
monitoring method is its intervention policy: a decision function determining
if and when to trigger an intervention on an ongoing case. Previous proposals
in this field rely on intervention policies that consider only the current
state of a given case. These approaches do not consider the tradeoff between
triggering an intervention in the current state, given the level of uncertainty
of the underlying predictive models, versus delaying the intervention to a
later state. Moreover, they assume that a resource is always available to
perform an intervention (infinite capacity). This paper addresses these gaps by
introducing a prescriptive process monitoring method that filters and ranks
ongoing cases based on prediction scores, prediction uncertainty, and causal
effect of the intervention, and triggers interventions to maximize a gain
function, considering the available resources. The proposal is evaluated using
a real-life event log. The results show that the proposed method outperforms
existing baselines regarding total gain.
