Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairness
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 71 references that are in the map. First posted on arXiv 6 Aug 2024.
Builds on
21 of 71 references.
References · 21
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- 2022Supervised Training of Conditional Monge Mapscited by 60
- 2019FlipTest: Fairness Testing via Optimal Transportcited by 51
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- 2022Active Bayesian Causal Inferencecited by 34
- 2019Fair Data Adaptation with Quantile Preservationcited by 29
9 more
- 2021Transport-based Counterfactual Modelscited by 27
- 2023Conditional Optimal Transport on Function Spacescited by 26
- 2016Causal transport in discrete time and applicationscited by 21
- 2021fairadapt: Causal Reasoning for Fair Data Pre-processingcited by 16
- 2023On Learning Necessary and Sufficient Causal Graphscited by 14
- 2023Learning for Counterfactual Fairness from Observational Datacited by 13
- 2020Multivariate Counterfactual Systems And Causal Graphical Modelscited by 9
- 2023Optimal transport and Wasserstein distances for causal modelscited by 9
- 2024FairPFN: Transformers Can do Counterfactual Fairnesscited by 8
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Citing papers per year: 2024: 1; 2025: 2.
3 papers.