arXiv Machine Learning July 27, 2026 By Oleksii Furman, {\L}ukasz Lenkiewicz, Marcel Musia{\l}ek, Maciej Zi\k{e}ba

CEL: Comprehensive Counterfactual Explanations Library and Benchmark

Read the original on arXiv Machine Learning →

arXiv:2607. 22045v1 Announce Type: new Abstract: Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

1d ago

Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments

arXiv:2608. 16747v1 Announce Type: cross Abstract: Many areas of AI research, such as language model interpretability and chain of thought faithfulness, seek to explain model behaviors.

By Adam Karvonen, Euan Ong, Subhash Kantamneni, Samuel Marks

llms agents safety

arXiv Machine Learning

Jul 9

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

arXiv:2601. 14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction.

By Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter, Melanie Hingle, Hassan Ghasemzadeh

llms fine-tuning multimodal safety

Hugging Face Trending Papers

Jun 16

ConTex: Reformulating Counterfactual Generation For Time Series Forecasting

Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights. However, current architectures do not inherently provide such information.

benchmarks

arXiv Machine Learning

Jun 17

ConTex: Reformulating Counterfactual Generation For Time Series Forecasting

arXiv:2606. 18049v1 Announce Type: new Abstract: Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights.

By Jan Voets, Hasan Tercan, Tobias Meisen, Sebastian Baum

benchmarks

arXiv Machine Learning

Jul 1

DISCOVER: A Solver for Distributional Counterfactual Explanations

arXiv:2603. 16436v2 Announce Type: replace Abstract: Counterfactual explanations (CE) explain model decisions by identifying input modifications that lead to different predictions.

By Yikai Gu, Lele Cao, Bo Zhao, Lei Lei, Lei You

safety

arXiv Machine Learning

Jul 31

Class-Aware Reinforcement Learning for Counterfactual Explanation Generation

arXiv:2607. 27905v1 Announce Type: new Abstract: Counterfactual explanations (CFEs) enhance the interpretability of black-box models by generating alternative instances with adjusted feature values that achieve a contrastive outcome.

By Muhammad Adil Saleem, Syed Ali Raza, Mary-Anne Williams

reinforcement-learning efficiency safety

Related stories

Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

ConTex: Reformulating Counterfactual Generation For Time Series Forecasting

ConTex: Reformulating Counterfactual Generation For Time Series Forecasting

DISCOVER: A Solver for Distributional Counterfactual Explanations

Class-Aware Reinforcement Learning for Counterfactual Explanation Generation