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- W4324044747 abstract "Prescriptive process monitoring methods seek to control the execution of a business process by triggering interventions, at runtime, to optimise one or more performance measure(s) such as cycle time or defect rate. Examples of interventions include, for example, using a premium shipping service to reduce cycle time in an order-to-cash process, or offering better loan conditions to increase the acceptance rate in a loan origination process. Each of these interventions comes with a cost. Thus, it is important to carefully select the set of cases to which an intervention is applied. The paper proposes a prescriptive process monitoring method that incorporates causal inference techniques to estimate the causal effect of triggering an intervention on each ongoing case of a process. Based on this estimate, the method triggers interventions according to a user-defined policy, taking into account the net gain of the interventions. The method is evaluated on four real-life data sets." @default.
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- W4324044747 date "2023-06-01" @default.
- W4324044747 modified "2023-10-12" @default.
- W4324044747 title "Prescriptive process monitoring based on causal effect estimation" @default.
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- W4324044747 doi "https://doi.org/10.1016/j.is.2023.102198" @default.
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