CIRCUS: A Causal Intervention-Based Framework for Enhancing Counterfactual Fairness in Trained Classifiers.
- DOI
- 10.1109/tnnls.2026.3670269
- Published
- 2026 Sep
- Container
- IEEE transactions on neural networks and learning systems
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2026.3670269,
title = {CIRCUS: A Causal Intervention-Based Framework for Enhancing Counterfactual Fairness in Trained Classifiers.},
author = {Yang Q and Deng Y and Huang J and Lu L and Zhou P and Min G},
year = {2026},
journal = {IEEE transactions on neural networks and learning systems},
doi = {10.1109/tnnls.2026.3670269},
url = {https://doi.org/10.1109/tnnls.2026.3670269}
}RIS
TY - JOUR TI - CIRCUS: A Causal Intervention-Based Framework for Enhancing Counterfactual Fairness in Trained Classifiers. AU - Yang Q AU - Deng Y AU - Huang J AU - Lu L AU - Zhou P AU - Min G PY - 2026 JO - IEEE transactions on neural networks and learning systems DO - 10.1109/tnnls.2026.3670269 UR - https://doi.org/10.1109/tnnls.2026.3670269 ER -
APA
Q, Y., Y, D., J, H., L, L., P, Z., & G, M. (2026). CIRCUS: A Causal Intervention-Based Framework for Enhancing Counterfactual Fairness in Trained Classifiers.. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/tnnls.2026.3670269
Source records
- pubmed · retrieved 2026-09-25T20:09:28.979Z