Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
Machine Learning
2025-04-29 v3 Methodology
Abstract
In this paper, we link two existing approaches to derive counterfactuals: adaptations based on a causal graph, and optimal transport. We extend "Knothe's rearrangement" and "triangular transport" to probabilistic graphical models, and use this counterfactual approach, referred to as sequential transport, to discuss fairness at the individual level. After establishing the theoretical foundations of the proposed method, we demonstrate its application through numerical experiments on both synthetic and real datasets.
Cite
@article{arxiv.2408.03425,
title = {Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness},
author = {Agathe Fernandes Machado and Arthur Charpentier and Ewen Gallic},
journal= {arXiv preprint arXiv:2408.03425},
year = {2025}
}