用因果+论据框架解释模型决策原因，更懂‘为什么’。
A Causal Argumentation Method for Explainability of Machine Learning Models
- 结合因果发现与论据框架，构建特征间支持/反对关系
- 通过半稳定语义找到能解释结果的特征组合
- 适合需要深层决策逻辑解释的场景，如医疗、金融
可解释人工智能（XAI）方法虽能识别影响模型预测的关键特征，却常无法说明为何做出特定决策。本文提出一种新方法，将因果推理与基于论据的推理相结合，以解释模型为何作出某些预测。首先利用因果发现方法识别变量间的因果关系，再将其转化为双极论据框架（BAF），表示特征间的支持与对立作用。通过半稳定语义，寻找能解释特定结果的特征扩展。我们在两个基准数据集上验证该方法，并与主流后处理解释方法对比，结果表明其在揭示决策依据方面更具深度。
原文摘要 · Abstract (English)
Explainable AI (XAI) methods identify which features are relevant to a model's predictions but often fail to clarify why certain decisions are made. In this work, we present a novel method that integrates causality with argument-based reasoning to explain why models may be making predictions. Our approach first identifies causal relationships among variables using causal discovery methods and then translates these into a Bipolar Argumentation Framework (BAF) to represent supportive and opposing interactions among features. By using semi-stable semantics, we find extensions of features that explain why certain outcomes may have been chosen. We demonstrate our method on two benchmark datasets and compare its results against standard post-hoc explainability approaches.
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