Unraveling the causal relationships among the execution of process activities is a crucial element in predicting the consequences of process interventions and making informed decisions regarding process improvements. Process discovery algorithms exploit time precedence as their main source of model derivation. Hence, a causal view can supplement process discovery, being a new perspective in which relations reflect genuine cause-effect dependencies among the tasks. This calls for faithful new techniques to discover the causal execution dependencies among the tasks in the process. To this end, our work offers a systematic approach to the unveiling of the 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：揭示流程活动执行之间的因果关系，是预测流程干预后果以及就流程改进做出明智决策的关键要素。流程发现算法主要利用时间先后顺序作为模型推导的源泉。因此，因果关系视角可以作为流程发现的补充，成为一种新型视角，其中关系反映任务间真实的因果依赖。这需要可靠的新技术来发现流程中任务间的因果执行依赖。为此，我们的工作提供了一种系统方法，通过利用现有基于活动时序的因果发现算法来揭示因果业务流程。此外，本研究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的模型的条件集，并展示了如何将该模型方法学地用于流程的合理分析。我们的方法在三种因果模式的背景下搜索这两种模型之间的此类差异，并推导出一种新视角，其中这些不一致性被标注在挖掘出的流程模型上。我们通过两种开放的流程挖掘算法、IBM流程挖掘工具以及LiNGAM因果发现技术来演示我们的方法。我们将该方法应用于一个合成数据集和两个开放基准数据集。