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Counterfactual Metarules for Local and Global Recourse (2405.18875v1)

Published 29 May 2024 in cs.AI

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 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.

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Authors (5)
  1. Tom Bewley (13 papers)
  2. Salim I. Amoukou (7 papers)
  3. Saumitra Mishra (19 papers)
  4. Daniele Magazzeni (42 papers)
  5. Manuela Veloso (105 papers)
Citations (1)

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