Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs

TL;DR

This paper combines process mining and causal machine learning to identify effective treatments in business processes, providing automated, causally-informed recommendations from event logs.

Contribution

It introduces a novel approach that integrates action rule mining with uplift trees to discover causal effects of treatments in process data.

Findings

Successfully applied to a loan application event log

Generated recommendations comparable to expert analysis

Demonstrated the effectiveness of causal methods in process mining

Abstract

This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome. Users classify the attributes in the event log into controllable and non-controllable, where the former correspond to attributes that can be altered during an execution of the process (the possible treatments). We use an action rule mining technique to identify treatments that co-occur with the outcome under some conditions. Since action rules are generated based on correlation rather than causation, we then use a causal machine learning technique, specifically uplift trees, to discover subgroups of cases for which a treatment has a high causal effect on the outcome after adjusting for confounding variables. We test the relevance of this approach using an event log of a loan application process and compare our findings with recommendations manually produced by process mining experts.