Counterfactual Metarules for Local and Global Recourse
Abstract
We introduce T-CREx, a novel model-agnostic method for local and global counterfactual explanation (CE), which summarises recourse options for both individuals and groups in the form of generalised rules. It leverages tree-based surrogate models to learn the counterfactual rules, alongside metarules denoting their regimes of optimality, providing both a global analysis of model behaviour and diverse recourse options for users. Experiments indicate that T-CREx achieves superior aggregate performance over existing rule-based baselines on a range of CE desiderata, while being orders of magnitude faster to run.
Cite
Text
Bewley et al. "Counterfactual Metarules for Local and Global Recourse." International Conference on Machine Learning, 2024.Markdown
[Bewley et al. "Counterfactual Metarules for Local and Global Recourse." International Conference on Machine Learning, 2024.](https://mlanthology.org/icml/2024/bewley2024icml-counterfactual/)BibTeX
@inproceedings{bewley2024icml-counterfactual,
title = {{Counterfactual Metarules for Local and Global Recourse}},
author = {Bewley, Tom and Amoukou, Salim I. and Mishra, Saumitra and Magazzeni, Daniele and Veloso, Manuela},
booktitle = {International Conference on Machine Learning},
year = {2024},
pages = {3707-3724},
volume = {235},
url = {https://mlanthology.org/icml/2024/bewley2024icml-counterfactual/}
}