Hans Weytjens

The work introduces SCOPE, a prescriptive process monitoring approach that combines causal learners (S-, T-, and RA-learners) with regret-based backward induction to learn sequential intervention policies aligned across multiple decision points directly from observational event logs, without building an MDP or augmenting data; on SimBank and on a new semi-synthetic benchmark SimBPIC17 built from the BPIC17_W event log, SCOPE outperforms baselines such as KMeans-Q and SEP-S in most experimental settings, with its advantage growing as training size and the number of decision points increase.

The work introduces SCOPE, a prescriptive process monitoring approach that combines causal learners (S-, T-, and RA-learners) with regret-based backward induction to learn sequential intervention policies aligned across multiple decision points directly from observational event logs, without building an MDP or augmenting data; on SimBank and on a new semi-synthetic benchmark SimBPIC17 built from the BPIC17_W event log, SCOPE outperforms baselines such as KMeans-Q and SEP-S in most experimental settings, with its advantage growing as training size and the number of decision points increase.