Feature recommendation for structural equation model discovery in process mining

Abstract

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 causal analysis can be quite involving. In this paper, we propose a method for finding the set of (aggregated) features with a possible causal effect on the problem. The causal 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 causal analysis. We have implemented the proposed method as a plugin in ProM, and we have evaluated it using real and synthetic event logs. These experiments show the validity and effectiveness of the proposed methods.