Proceedings

Optimal Transport on Categorical Data for Conterfactuals Using Compositional Data and Dirichlet Transport

Agathe Fernandes Machado, Ewen Gallic, Arthur Charpentier

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5136-5144.

https://doi.org/10.24963/ijcai.2025/572

Keywords

  • Uncertainty in AI: UAI: Causality, structural causal models and causal inference
  • Machine Learning: ML: Causality
  • AI Ethics, Trust, Fairness: ETF: Fairness and diversity
  • Uncertainty in AI: UAI: Tractable probabilistic models

Abstract

Recently, optimal transport-based approaches have gained attention for deriving counterfactuals, e.g., to quantify algorithmic discrimination. However, in the general multivariate setting, these methods are often opaque and difficult to interpret. To address this, alternative methodologies have been proposed, using causal graphs combined with iterative quantile regressions or sequential transport to examine fairness at the individual level, often referred to as "counterfactual fairness." Despite these advancements, transporting categorical variables remains a significant challenge in practical applications with real datasets. In this paper, we propose a novel approach to address this issue. Our method involves (1) converting categorical variables into compositional data and (2) transporting these compositions within the probabilistic simplex of the Euclidean space. We demonstrate the applicability and effectiveness of this approach through an illustration on real-world data, and discuss limitations.

Agathe Fernandes Machado, Ewen Gallic, and Arthur Charpentier. 2025. Optimal Transport on Categorical Data for Conterfactuals Using Compositional Data and Dirichlet Transport. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 5136–5144. https://doi.org/10.24963/ijcai.2025/572
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