Counterfactual Fairness

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Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness
Oct 1, 2026Junhyuk Huh, Seoungbin Bae, Dabeen LeeMulti-Armed Bandits Minimax Regret
Faculty of Mathematics, University of Cambridge · Department of Industrial & Systems Engineering, KAIST · Department of Mathematical Sciences, Seoul National University
Counterfactual Fairness Audits of Multi-Step Clinical LLM Agents Require a Measured Per-Action Instability Floor
Sep 2, 2026Rohith Reddy Bellibaltu, Manpreet Singh, Deepak Parashar +1Counterfactual Evaluation Algorithmic Fairness
Florida International University · Boston University, Boston, MA 02215 USA · Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India +1
A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
Aug 9, 2026Jianhan Zhang, Jitao Wang, John D. Piette +3Causal Counterfactual Generation Algorithmic Fairness
University of Michigan · LSE
GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits
Aug 3, 2026Nitish Nagesh, Elahe Khatibi, Thomas Dean Hughes +3Algorithmic Auditing Algorithmic Fairness
Department of Computer Science, University of California, Irvine, Irvine, CA, USA · University of Orléans, Orléans, France
Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study
Jul 7, 2026Sangwon Jung, Sumin Yu, Sanghyuk Chun +1Algorithmic Fairness Counterfactual Fairness
Department of Electrical and Computer Engineering, Seoul National University · NAVER AI Lab · ASRI/INMC/IPAI/AIIS, Seoul National University
Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms
Jul 6, 2026Lea Multerer, Michele Inchingolo, David Kletz +3Algorithmic Fairness Counterfactual Fairness
IDSIA, Switzerland · UBS Switzerland AG and its affiliates, Germany
AgentFairBench: Do LLM Agents Discriminate When They Act?
Jun 15, 2026Triveni Morla, Rohith Reddy Bellibaltu, Manpreet Singh +1Algorithmic Fairness LLM Agent Evaluation
Florida International University, Miami, FL 33199 USA · Boston University, Boston, MA 02215 USA · Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, Bihar, India
Detecting and Mitigating Bias by Treating Fairness as a Symmetry Operation
Jun 2, 2026Nishit SinghDebiased ML Algorithmic Fairness
Birla Institute of Technology and Science, Pilani.
COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
May 30, 2026Sheng'en Li, Dongmian ZouAlgorithmic Fairness Counterfactual Fairness
Zu Chongzhi Center and DIRC, Duke Kunshan University, Kunshan, China.
COFT: Counterfactual-Conformal Decoding for Fair Chain-of-Thought Reasoning in Large Language Models
May 28, 2026Arya Fayyazi, Mehdi Kamal, Massoud PedramSocial Bias in Language Models Counterfactual Fairness
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Counterfactually Fair Regression via Optimal Transport
May 27, 2026M. Generali Lince, S. Gaucher, J-J. Vie +1Algorithmic Fairness Counterfactual Fairness
Inria, Soda team · CREST, ENSAE, IP Paris · CMAP, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris +1
Automatic Causal Fairness Analysis with LLM-Generated Reporting
Apr 29, 2026Alessia Berarducci, Eric Rossetto, Alessandro Antonucci +1Algorithmic Fairness Counterfactual Fairness
Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), USI-SUPSI, Lugano, Switzerland
Efficient and Interpretable Transformer for Counterfactual Fairness
Apr 29, 2026Panyi Dong, Zhiyu QuanTransformer Interpretability Transformer
Acturial and Risk Mangament Sciences, University of Illinois Urbana-Champaign, Champaign, Illinois, USA.
SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence
Dec 6, 2024Bowei Tian, Yexiao He, Ziyao Wang +3Debiased ML Algorithmic Fairness
University of Maryland, College Park, MD 20742, USA · Clemson University, Clemson, SC 29634, USA

Oct 1, 2026, cs.LGJ/K move · Enter open · S save

Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness

Junhyuk Huh, Seoungbin Bae, Dabeen Lee

Faculty of Mathematics, University of Cambridge · Department of Industrial & Systems Engineering, KAIST · Department of Mathematical Sciences, Seoul National University

Multi-Armed Bandits Minimax Regret Algorithmic Fairness Counterfactual Fairness Regret Minimization

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We study causal logistic bandits with counterfactual fairness constraints. The causal structure is given through known factual and counterfactual feature maps that share an unknown logistic reward parameter, but the learner observes only factual rewards. Consequently, the directions determining counterfactual feasibility need not be identifiable from the available feedback. The closest prior analyses either omit a coverage condition or impose a comparatively strong one, and do not establish matching lower bounds. We first show that some coverage condition is necessary: without a coverage-type restriction, factually indistinguishable environments with different optimal fair actions force

Ω(T)

expected joint loss. Under a weaker full-rank condition on the factual covariance pooled across actions, we identify a target-specific information scale

V_\star

that measures the difficulty of estimating rewards and counterfactual effects from factual feedback. We construct worst-case families satisfying this condition on which every policy incurs expected joint loss

Ω\left(\left[V_\star\min\{\log K,d\}\right]^{1/3}T^{2/3}\right)

. We also give an explore--then--exploit procedure tuned using

V_\star

and an adaptive algorithm that does not require its value. Both algorithms achieve

\max\{R_T,V_T\}=\widetilde{O}\left(\left[V_\star\min\{\log K,d\}\right]^{1/3}T^{2/3}+κd/σ_0^2\right)

, where

R_T

is regret relative to the best fair action and

V_T

denotes the cumulative stage-wise positive violations. Thus the upper and lower bounds match in their leading dependence on

T

V_\star

, and

\min\{\log K,d\}

, up to logarithmic factors.

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