The WHY in Business Processes: Discovery of Causal Execution Dependencies

TL;DR

This paper introduces a systematic approach to uncover causal dependencies in business processes by combining causal discovery algorithms with process mining, enhancing understanding of genuine cause-effect relationships among activities.

Contribution

It proposes a novel methodology that integrates causal discovery with process mining to identify and analyze causal execution dependencies and discrepancies in process models.

Findings

Successfully applied to synthetic and benchmark datasets

Identified discrepancies between causal and process models

Enhanced process understanding through causal annotations

Abstract

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 to a synthesized dataset and two open benchmark datasets.