The WHY in Business Processes: Discovery of Causal Execution Dependencies

A crucial element in predicting the outcomes of process interventions and making informed decisions about the process is unraveling the genuine relationships between the execution of process activities. Contemporary process discovery algorithms exploit time precedence as their main source of model derivation. Such reliance can sometimes be deceiving from a causal perspective. This calls for faithful new techniques to discover the true execution dependencies among the tasks in the process. To this end, our work offers a systematic approach to the unveiling of the true causal business process by leveraging an existing causal discovery algorithm over activity timing. In addition, this work delves into a set of conditions under which process mining discovery algorithms generate a model that is incongruent with the causal business process model, and shows how the latter model can be methodologically employed for a sound analysis of the process. Our methodology searches for such discrepancies between the two models in the context of three causal patterns, and derives a new view in which these inconsistencies are annotated over the mined process model. We demonstrate our methodology employing two open process mining algorithms, the IBM Process Mining tool, and the LiNGAM causal discovery technique. We apply it on a synthesized dataset and on two open benchmark data sets.