Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
This work addresses the need for causal insights in process mining to improve decision-making in business operations, representing an incremental advancement by integrating existing techniques.
The paper tackles the problem of generating case-level recommendations to maximize outcome probability in business processes by combining action rule mining with causal machine learning, specifically uplift trees, to identify treatments with high causal effects, and tests it on a loan application event log, comparing results with expert recommendations.
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.