# Counterfactual Explanations for Predictive Business Process Monitoring

arXiv: https://arxiv.org/abs/2202.12018 · HTML: https://mlsanity.com/paper/2202.12018/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 16 of 16)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Outcome-Oriented Predictive Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/1707.06766/) (2017)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Clustering-Based Predictive Process Monitoring](https://mlsanity.com/paper/1506.01428/) (2015)
- [An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction](https://mlsanity.com/paper/1811.00062/) (2018)
- [XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP](https://mlsanity.com/paper/2008.07993/) (2020)
- [AI Trust in business processes: The need for process-aware explanations](https://mlsanity.com/paper/2001.07537/) (2020)

## Built on by (most cited first, 12 of 12)

- [An Explainable Decision Support System for Predictive Process Analytics](https://mlsanity.com/paper/2207.12782/) (2022)
- [CREATED: Generating Viable Counterfactual Sequences for Predictive Process Analytics](https://mlsanity.com/paper/2303.15844/) (2023)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes](https://mlsanity.com/paper/2403.09232/) (2024)
- [A Framework for Extracting and Encoding Features from Object-Centric Event Data](https://mlsanity.com/paper/2209.01219/) (2022)
- [Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring](https://mlsanity.com/paper/2403.11642/) (2024)
- [Generating Counterfactual Explanations Under Temporal Constraints](https://mlsanity.com/paper/2503.01792/) (2025)
- [XABPs: Towards eXplainable Autonomous Business Processes](https://mlsanity.com/paper/2507.23269/) (2025)
- [Counterfactual Strategies for Markov Decision Processes](https://mlsanity.com/paper/2505.09412/) (2025)
- [Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles](https://mlsanity.com/paper/2303.14939/) (2023)
- [The Role of Explanation Styles and Perceived Accuracy on Decision Making in Predictive Process Monitoring](https://mlsanity.com/paper/2506.16617/) (2025)
- [KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education](https://mlsanity.com/paper/2601.09156/) (2026)

## Abstract (arXiv)

Predictive business process monitoring increasingly leverages sophisticated prediction models. Although sophisticated models achieve consistently higher prediction accuracy than simple models, one major drawback is their lack of interpretability, which limits their adoption in practice. We thus see growing interest in explainable predictive business process monitoring, which aims to increase the interpretability of prediction models. Existing solutions focus on giving factual explanations.While factual explanations can be helpful, humans typically do not ask why a particular prediction was made, but rather why it was made instead of another prediction, i.e., humans are interested in counterfactual explanations. While research in explainable AI produced several promising techniques to generate counterfactual explanations, directly applying them to predictive process monitoring may deliver unrealistic explanations, because they ignore the underlying process constraints. We propose LORELEY, a counterfactual explanation technique for predictive process monitoring, which extends LORE, a recent explainable AI technique. We impose control flow constraints to the explanation generation process to ensure realistic counterfactual explanations. Moreover, we extend LORE to enable explaining multi-class classification models. Experimental results using a real, public dataset indicate that LORELEY can approximate the prediction models with an average fidelity of 97.69% and generate realistic counterfactual explanations.