Case Level Counterfactual Reasoning in Process Mining
This work addresses the need for causal insights to improve processes in process mining, offering a novel approach beyond standard diagnostics.
The paper tackles the problem of moving beyond correlation to causality in process mining by introducing structural equation models and counterfactual reasoning, enabling reasoning over event logs and process interventions, with implementation as a ProM plug-in and evaluation on multiple datasets.
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.