# Case Level Counterfactual Reasoning in Process Mining

arXiv: https://arxiv.org/abs/2102.13490 · HTML: https://mlsanity.com/paper/2102.13490/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 25 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Efficient Search for Diverse Coherent Explanations](https://mlsanity.com/paper/1901.04909/) (2019)
- [Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs](https://mlsanity.com/paper/2009.01561/) (2020)

## Built on by (most cited first, 4 of 4)

- [Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction](https://mlsanity.com/paper/2105.07111/) (2021)
- [Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning](https://mlsanity.com/paper/2303.03572/) (2023)
- [CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analytics](https://mlsanity.com/paper/2303.15844/) (2023)
- [Feature Recommendation for Structural Equation Model Discovery in Process Mining](https://mlsanity.com/paper/2108.07795/) (2021)

## Abstract (arXiv)

Process mining is widely used to diagnose processes and uncover performance and compliance problems. It is also possible to see relations between different behavioral aspects, e.g., cases that deviate more at the beginning of the process tend to get delayed in the later part of the process. However, correlations do not necessarily reveal causalities. Moreover, standard process mining diagnostics do not indicate how to improve the process. This is the reason we advocate the use of structural equation models and counterfactual reasoning. We use results from causal inference and adapt these to be able to reason over event logs and process interventions. We have implemented the approach as a ProM plug-in and have evaluated it on several data sets.