Tootfinder

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[2/2]:
- FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
Christos Fragkathoulas, Vasiliki Papanikou, Evaggelia Pitoura, Evimaria Terzi

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new/
[1/1]:
Epistemic vs. Counterfactual Fairness in Allocation of Resources
https…

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Lucas de Lara (IECL)
https://arxiv.org/abs/2507.16370

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Counterfactual reasoning aims at answering contrary-to-fact questions like ''Would have Alice recovered had she taken aspirin?'' and corresponds to the most fine-grained layer of causation. Critically, while many counterfactual statements cannot be falsified -- even by randomized experiments -- they underpin fundamental concepts like individual-wise fairness. Therefore, providing models to formalize and implement counterfactual beliefs remains a fundamental scientific problem. In the Markovian …

A Proof System with Causal Labels (Part II): checking Counterfactual Fairness
Leonardo Ceragioli, Giuseppe Primiero
https://arxiv.org/abs/2507.14655 https:…

A Proof System with Causal Labels (Part II): checking Counterfactual Fairness
In this article we propose an extension to the typed natural deduction calculus TNDPQ to model verification of counterfactual fairness in probabilistic classifiers. This is obtained formulating specific structural conditions for causal labels and checking that evaluation is robust under their variation.

Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
Zengyi Wo, Chang Liu, Yumeng Wang, Minglai Shao, Wenjun Wang
https://arxiv.org/abs/2508.14683 https://…

Improving Fairness in Graph Neural Networks via Counterfactual Debiasing
Graph Neural Networks (GNNs) have been successful in modeling graph-structured data. However, similar to other machine learning models, GNNs can exhibit bias in predictions based on attributes like race and gender. Moreover, bias in GNNs can be exacerbated by the graph structure and message-passing mechanisms. Recent cutting-edge methods propose mitigating bias by filtering out sensitive information from input or representations, like edge dropping or feature masking. Yet, we argue that such st…