xai-cola: A Python library for sparsifying counterfactual explanations

• URL: http://arxiv.org/abs/2602.21845v1
• Date: Wed, 25 Feb 2026 12:25:29 GMT
• Title: xai-cola: A Python library for sparsifying counterfactual explanations
• Authors: Lin Zhu, Lei You,
• Abstract summary: xai-cola provides an end-to-end pipeline for sparsifying explanations (CEs) produced by arbitrary generators.<n>It offers a documented API that takes as input raw data in pandas DataFrame form, a preprocessing object (for standardization and encoding), and a trained scikit-learn or PyTorch model.<n> Empirical experiments indicate that xai-cola produces sparser counterfactuals across several CE generators, reducing the number of modified features by up to 50% in our setting.
• Score: 10.00040471953469
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual explanation (CE) is an important domain within post-hoc explainability. However, the explanations generated by most CE generators are often highly redundant. This work introduces an open-source Python library xai-cola, which provides an end-to-end pipeline for sparsifying CEs produced by arbitrary generators, reducing superfluous feature changes while preserving their validity. It offers a documented API that takes as input raw tabular data in pandas DataFrame form, a preprocessing object (for standardization and encoding), and a trained scikit-learn or PyTorch model. On this basis, users can either employ the built-in or externally imported CE generators. The library also implements several sparsification policies and includes visualization routines for analysing and comparing sparsified counterfactuals. xai-cola is released under the MIT license and can be installed from PyPI. Empirical experiments indicate that xai-cola produces sparser counterfactuals across several CE generators, reducing the number of modified features by up to 50% in our setting. The source code is available at https://github.com/understanding-ml/COLA.

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