  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 human-readable rules. It leverages
tree-based surrogate models to learn the counterfactual rules, alongside
'metarules' denoting their regions 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.
