  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 later 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 structural equation models and 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.
