  The shift from the understanding and prediction of processes to their
optimization offers great benefits to businesses and other organizations.
Precisely timed process interventions are the cornerstones of effective
optimization. Prescriptive process monitoring (PresPM) is the sub-field of
process mining that concentrates on process optimization. The emerging PresPM
literature identifies state-of-the-art methods, causal inference (CI) and
reinforcement learning (RL), without presenting a quantitative comparison. Most
experiments are carried out using historical data, causing problems with the
accuracy of the methods' evaluations and preempting online RL. Our contribution
consists of experiments on timed process interventions with synthetic data that
renders genuine online RL and the comparison to CI possible, and allows for an
accurate evaluation of the results. Our experiments reveal that RL's policies
outperform those from CI and are more robust at the same time. Indeed, the RL
policies approach perfect policies. Unlike CI, the unaltered online RL approach
can be applied to other, more generic PresPM problems such as next best
activity recommendations. Nonetheless, CI has its merits in settings where
online learning is not an option.
