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

Feature Recommendation for Structural Equation Model Discovery in Process Mining