  This paper proposes an approach to analyze an event log of a business process
in order to generate case-level recommendations of treatments that maximize the
probability of a given outcome. Users classify the attributes in the event log
into controllable and non-controllable, where the former correspond to
attributes that can be altered during an execution of the process (the possible
treatments). We use an action rule mining technique to identify treatments that
co-occur with the outcome under some conditions. Since action rules are
generated based on correlation rather than causation, we then use a causal
machine learning technique, specifically uplift trees, to discover subgroups of
cases for which a treatment has a high causal effect on the outcome after
adjusting for confounding variables. We test the relevance of this approach
using an event log of a loan application process and compare our findings with
recommendations manually produced by process mining experts.
