  Process mining techniques can help organizations to improve their operational
processes. Organizations can benefit from process mining techniques in finding
and amending the root causes of performance or compliance problems. Considering
the volume of the data and the number of features captured by the information
system of today's companies, the task of discovering the set of features that
should be considered in root cause analysis can be quite involving. In this
paper, we propose a method for finding the set of (aggregated) features with a
possible effect on the problem.
  The root cause analysis task is usually done by applying a machine learning
technique to the data gathered from the information system supporting the
processes. To prevent mixing up correlation and causation, which may happen
because of interpreting the findings of machine learning techniques as causal,
we propose a method for discovering the structural equation model of the
process that can be used for root cause analysis. We have implemented the
proposed method as a plugin in ProM and we have evaluated it using two real and
synthetic event logs. These experiments show the validity and effectiveness of
the proposed methods.
