用双编码方法发现特征间因果关系，提升可解释AI的可靠性
Causal Discovery for Explainable AI: A Dual-Encoding Approach
- 采用两种互补编码策略并行运行约束算法
- 在泰坦尼克数据集上识别出符合常识的因果结构
- 适合需要可信因果分析的可解释AI应用场景
理解特征间的因果关系是解释机器学习模型决策的基础。然而，传统因果发现方法在处理类别变量时因条件独立性检验的数值不稳定性而面临挑战。我们提出一种双编码因果发现方法，通过运行具有互补编码策略的约束基算法，并利用多数投票融合结果来克服这些局限。该方法应用于泰坦尼克数据集，成功识别出与已有可解释方法一致的因果结构，验证了其有效性。
原文摘要 · Abstract (English)
Understanding causal relationships among features is fundamental for explaining machine learning model decisions. However, traditional causal discovery methods face challenges with categorical variables due to numerical instability in conditional independence testing. We propose a dual-encoding causal discovery approach that addresses these limitations by running constraint-based algorithms with complementary encoding strategies and merging results through majority voting. Applied to the Titanic dataset, our method identifies causal structures that align with established explainable methods.
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