发表机构
Faculty of Business, Kyoritsu Women’s University（共立女子大学商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究从广义贝叶斯视角重新阐释反事实解释，提出DP-GBCE，引入两种决策规则并扩展至多模型后验混合，定义评估指标，通过实验量化决策规则的权衡。
AI 中文摘要
反事实解释（Counterfactual Explanations, CEs）通过确定输入所需的最小变化以获得期望输出，增强了机器学习模型的可解释性。尽管CE通常被表述为最小化距离的问题，但该表述的理论基础受到的关注有限。我们证明，基于距离最小化的CE在数学上等价于广义贝叶斯框架内吉布斯后验的最大后验（Maximum A Posteriori, MAP）估计，尤其在使用基于距离的先验时，我们将该表述称为距离先验广义贝叶斯CE（Distance-Prior Generalized Bayes CE, DP-GBCE）。基于这一后验视角，我们在统一框架内引入了两种超越MAP的决策规则：最小化期望决策损失的贝叶斯决策，以及风险规避型决策规则CVaR-CE。我们还提出了一种扩展方法，使用贝叶斯模型权重混合多个模型的后验分布，从而考虑模型多重性——即多个模型具有可比预测性能的情况。最后，我们定义了用于评估单个CE和后验分布整体的指标，并通过在模拟数据和Google Trends数据上的实验量化了这些决策规则之间的权衡。
英文摘要
Counterfactual explanations (CEs) enhance the interpretability of machine learning models by identifying the smallest change to an input required to obtain a desired output. Although CEs are conventionally formulated as a distance-minimization problem, the theoretical basis of this formulation has received limited attention. We show that a distance-minimization-based CE is mathematically equivalent to the maximum a posteriori (MAP) estimate of a Gibbs posterior within the generalized Bayes framework, specifically when a distance-based prior is used. We call this formulation the Distance-Prior Generalized Bayes CE (DP-GBCE). Building on this posterior perspective, we introduce two decision rules beyond MAP within a unified framework: a Bayes decision that minimizes expected decision loss and CVaR-CE, a risk-averse decision rule. We also propose an extension that uses Bayesian model weights to mix the posterior distributions of multiple models, thereby accounting for model multiplicity, where several models have comparable predictive performance. Finally, we define metrics for evaluating both individual CEs and the posterior distribution as a whole, and use experiments on simulated data and Google Trends data to quantify the trade-offs among the decision rules.
Comments25 pages,5 figures
