Linkohr LM, Bourguiba AR, Ließmann A (2026)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2026
Conference Proceedings Title: Proceedings of the 34th European Conference on Information Systems
Event location: Milan, Italy
URI: https://aisel.aisnet.org/ecis2026/bus_analytics/bus_analytics/4/
Prescriptive process monitoring (PSPM) aims at recommending interventions that improve business process performance, yet the vast majority of existing approaches are correlation-based and unable to define the actions that actually cause better outcomes. This limitation is especially critical in IT service management (ITSM), where effective resource allocation is crucial for timely resolutions. Using a DSR approach, we develop a PSPM artifact that utilizes causal inference and double machine learning to provide KPI-oriented resource reallocation recommendations during runtime through conditional average treatment effect (CATE) estimation. The artifact combines offline causal modeling with online preprocessing to generate resource reallocation suggestions. A preliminary evaluation using real-world ITSM data from a company in the medical technology field shows initial promising results for causal resource reallocation. We outline a two-stage evaluation plan combining performance metrics, expert validation, and scenario-based KPI comparison to assess the system’s effectiveness.
APA:
Linkohr, L.-M., Bourguiba, A.R., & Ließmann, A. (2026). Towards Causal Resource Reallocation in Prescriptive Process Monitoring. In Proceedings of the 34th European Conference on Information Systems. Milan, Italy.
MLA:
Linkohr, Lisa-Marie, Ahmed Rayen Bourguiba, and Annina Ließmann. "Towards Causal Resource Reallocation in Prescriptive Process Monitoring." Proceedings of the European Conference on Information Systems, Milan, Italy 2026.
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