  Process mining is widely used to diagnose processes and uncover performance
and compliance problems. It is also possible to see relations between different
behavioral aspects, e.g., cases that deviate more at the beginning of the
process tend to get delayed in the last part of the process. However,
correlations do not necessarily reveal causalities. Moreover, standard process
mining diagnostics do not indicate how to improve the process. This is the
reason we advocate the use of \emph{structural equation models} and
\emph{counterfactual reasoning}. We use results from causal inference and adapt
these to be able to reason over event logs and process interventions. We have
implemented the approach as a ProM plug-in and have evaluated it on several
data sets. Our ProM plug-in produces recommendations that indicate how specific
cases could have been handled differently to avoid a performance or compliance
problem.
