A Generalized-Bayes Perspective on Counterfactual Explanations: Posterior-Based Decision-Making and Evaluation

arXiv:2607.29077 · cs.AI, stat.ML · Submitted 2026-07-31 · Read on arXiv

Keita Kinjo

cs.AI, stat.ML

Submitted: 2026-07-31

Comments: 25 pages,5 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Robustness — 21× in this paper · explained in 6 episodes
• XGBoost — 8× in this paper · explained in 3 episodes
• Bayesian Inference — 5× in this paper · explained in 6 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Unified Framework — 4× in this paper · explained in 6 episodes
• Bayesian Framework — 3× in this paper · explained in 3 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Accountability — 2× in this paper · explained in 2 episodes
• Adversarial Examples — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Conditional Independence — 1× in this paper · explained in 2 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Distribution Shift — 1× in this paper · explained in 12 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 12 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Multi-objective Optimization — 1× in this paper · explained in 5 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 6 episodes

Sources

• A Primer on PAC-Bayesian Learning
• Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization

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