Data-Driven Causal Decision Support for Business Process Management

Control-flow and resource assignment decisions influence business processes. Recorded process data can be used to identify which decisions are informed by data to predict their outcome, and to guide interventions as part of a what-if analysis. The latter requires causal models that explain decisions. Yet, existing methods are limited: they focus on control-flow decisions only, ignore potential confounders, and use ad-hoc methods to resolve causal conflicts. We fill this gap, by introducing a causal decision modeling framework which uncovers confounding effects, and captures resource decisions. Moreover, we provide a process-aware causal discovery algorithm that takes process precedence into account. In addition, we employ domain knowledge to include unobserved factors. We address the problem of identification, conduct interventional outcome prediction and improve decision-making by acquiring unavailable data to maximize the utility of interventions. We demonstrate the feasibility of our approach through a set of experiments on synthetically generated and real-world datasets.