# The WHY in Business Processes: Discovery of Causal Execution Dependencies

arXiv: https://arxiv.org/abs/2310.14975 · HTML: https://mlsanity.com/paper/2310.14975/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models](https://mlsanity.com/paper/2011.01625/) (2020)
- [Causal Discovery in the Presence of Missing Data](https://mlsanity.com/paper/1807.04010/) (2018)
- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs](https://mlsanity.com/paper/2009.01561/) (2020)
- [When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints](https://mlsanity.com/paper/2206.07745/) (2022)
- [Causal Process Mining from Relational Databases with Domain Knowledge](https://mlsanity.com/paper/2202.08314/) (2022)
- [Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes](https://mlsanity.com/paper/2212.03710/) (2022)

## Built on by (most cited first, 7 of 7)

- [Taming Uncertainty via Automation: Observing, Analyzing, and Optimizing Agentic AI Systems](https://mlsanity.com/paper/2507.11277/) (2025)
- [Agentic AI Process Observability: Discovering Behavioral Variability](https://mlsanity.com/paper/2505.20127/) (2025)
- [Agentic Business Process Management: A Research Manifesto](https://mlsanity.com/paper/2603.18916/) (2026)
- [Towards a Benchmark for Causal Business Process Reasoning with LLMs](https://mlsanity.com/paper/2406.05506/) (2024)
- [Selecting the Right LLM for eGov Explanations](https://mlsanity.com/paper/2504.21032/) (2025)
- [XABPs: Towards eXplainable Autonomous Business Processes](https://mlsanity.com/paper/2507.23269/) (2025)
- [The WHY in Business Processes: Unification of Causal Process Models](https://mlsanity.com/paper/2505.22871/) (2025)

## Abstract (arXiv)

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.