xai-cola: A Python library for sparsifying counterfactual explanations
This work provides a tool for improving the interpretability of machine learning models in domains like tabular data analysis, though it is incremental as it builds on existing counterfactual explanation methods.
The paper tackles the problem of redundant counterfactual explanations by introducing xai-cola, a Python library that sparsifies these explanations, reducing modified features by up to 50% while preserving validity.
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.