Show simple item record

dc.contributor.authorDasht Bozorgi, Z
dc.contributor.authorTeinemaa, I
dc.contributor.authorDumas, M
dc.contributor.authorLa Rosa, M
dc.contributor.authorPolyvyanyy, A
dc.contributor.editorvanDongen, B
dc.contributor.editorMontali, M
dc.contributor.editorWynn, MT
dc.date.accessioned2021-02-02T22:18:19Z
dc.date.available2021-02-02T22:18:19Z
dc.date.issued2020-10-22
dc.identifier.citationDasht Bozorgi, Z., Teinemaa, I., Dumas, M., La Rosa, M. & Polyvyanyy, A. (2020). Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs. vanDongen, B (Ed.) Montali, M (Ed.) Wynn, MT (Ed.) Proceedings of the 2020 2nd International Conference on Process Mining (ICPM), 00, pp.129-136. IEEE. https://doi.org/10.1109/ICPM49681.2020.00028.
dc.identifier.isbn9781728198323
dc.identifier.urihttp://hdl.handle.net/11343/258903
dc.description.abstractThis 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.
dc.languageEnglish
dc.publisherIEEE
dc.source2020 2nd International Conference on Process Mining (ICPM)
dc.titleProcess Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
dc.typeConference Paper
dc.identifier.doi10.1109/ICPM49681.2020.00028
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleProceedings of the 2020 2nd International Conference on Process Mining (ICPM)
melbourne.source.volume00
melbourne.source.pages129-136
melbourne.identifier.arcDP180102839
melbourne.elementsid1458791
melbourne.contributor.authorDasht Bozorgi, Zahra
melbourne.contributor.authorLa Rosa, Marcello
melbourne.contributor.authorPolyvyanyy, Artem
melbourne.identifier.fundernameidAustralian Research Council, DP180102839
melbourne.event.locationPadua, Italy
melbourne.accessrightsOpen Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record