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.

翻译：预测流程干预结果并做出明智流程决策的关键在于揭示流程活动执行之间的真实关系。当前流程发现算法主要利用时间先后顺序作为模型推导的主要依据，但从因果视角来看，这种依赖有时可能具有误导性。这要求我们开发可靠的新技术来发现流程任务之间的真实执行依赖关系。为此，本文提出了一种系统化方法，通过利用基于活动时序的现有因果发现算法来揭示真实的因果业务流程。此外，本文深入探讨了流程挖掘发现算法生成与因果业务流程模型不一致模型的条件，并展示了如何方法化地运用后者对流程进行合理分析。我们的方法在三种因果模式的背景下搜索两个模型之间的此类差异，并推导出一种新视图，将这些不一致性标注在挖掘出的流程模型上。我们使用两种开放流程挖掘算法（IBM流程挖掘工具和LiNGAM因果发现技术）演示了该方法，并将其应用于一个合成数据集和两个开放基准数据集。