Case Level Counterfactual Reasoning in Process Mining

TL;DR

This paper introduces a novel approach using structural equation models and counterfactual reasoning in process mining to identify causal relationships and suggest process improvements, implemented as a ProM plug-in and evaluated on multiple datasets.

Contribution

It adapts causal inference techniques for process mining, enabling case-level counterfactual analysis and causal diagnostics beyond correlation.

Findings

Effective causal analysis demonstrated on real datasets

Identifies potential process interventions for improvement

Provides a new tool for causal reasoning in process mining

Abstract

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.