ICML 2024poster3 citations
Counterfactual Metarules for Local and Global Recourse
Tom Bewley, Salim I. Amoukou, Saumitra Mishra, Daniele Magazzeni, Manuela Veloso
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.
BibTeX
@inproceedings{
bewley2024counterfactual,
title={Counterfactual Metarules for Local and Global Recourse},
author={Tom Bewley and Salim I. Amoukou and Saumitra Mishra and Daniele Magazzeni and Manuela Veloso},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=Ad9msn1SKC}
}