2020/09/03 by Zahra Dasht Bozorgi, Bozorgi, Zahra Dasht, Irene Teinemaa +7 · 1 citation
Business, Management and Accounting · Decision Sciences · #Big Data and Business Intelligence #Business Process Modeling and Analysis #Data Quality and Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.2009.01561
openalex publication_date 2020/09/03 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
This paper proposes an approach to analyze an event log of a business process\nin order to generate case-level recommendations of treatments that maximize the\nprobability of a given outcome. Users classify the attributes in the event log\ninto controllable and non-controllable, where the former correspond to\nattributes that can be altered during an execution of the process (the possible\ntreatments). We use an action rule mining technique to identify treatments that\nco-occur with the outcome under some conditions. Since action rules are\ngenerated based on correlation rather than causation, we then use a causal\nmachine learning technique, specifically uplift trees, to discover subgroups of\ncases for which a treatment has a high causal effect on the outcome after\nadjusting for confounding variables. We test the relevance of this approach\nusing an event log of a loan application process and compare our findings with\nrecommendations manually produced by process mining experts.\n