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The shift from the understanding and prediction of processes to theiroptimization offers great benefits to businesses and other organizations.Precisely timed process interventions are the cornerstones of effectiveoptimization. Prescriptive process monitoring (PresPM) is the sub-field ofprocess mining that concentrates on process optimization. The emerging PresPMliterature identifies state-of-the-art methods, causal inference (CI) andreinforcement learning (RL), without presenting a quantitative comparison. Mostexperiments are carried out using historical data, causing problems with theaccuracy of the methods' evaluations and preempting online RL. Our contributionconsists of experiments on timed process interventions with synthetic data thatrenders genuine online RL and the comparison to CI possible, and allows for anaccurate evaluation of the results. Our experiments reveal that RL's policiesoutperform those from CI and are more robust at the same time. Indeed, the RLpolicies approach perfect policies. Unlike CI, the unaltered online RL approachcan be applied to other, more generic PresPM problems such as next bestactivity recommendations. Nonetheless, CI has its merits in settings whereonline learning is not an option.

Timing Process Interventions with Causal Inference and Reinforcement Learning