Counterfactual Reasoning for Process Optimization Using Structural Causal Models

Business processes are complex and involve the execution of various steps using different resources that can be shared across various tasks. Processes require analysis and process owners need to constantly look for methods to improve process performance indicators. It is nontrivial to quantify the improvement of a proposed change, without implementing or conducting randomized controlled trials. In several cases, the cost and time for implementing and evaluating the benefits of these changes are high. To address this, we propose a principled framework using Structural Causal Models which formally codify existing causeeffect assumptions about the process, control confounding and answer "what if" questions with observational data. We formally define an end to end methodology which takes process execution logs and specified BPMN model as inputs for structural causal model discovery and for performing counterfactual reasoning. We show that exploiting the process specification for causal discovery automatically ensures the inclusion of subject matter expertise, and also provides an effective computational methodology. We illustrate the effectiveness of our approach by answering intervention and counterfactual questions on example process models.