Causal Discovery for Explainable AI: A Dual-Encoding Approach
This work addresses a specific problem in explainable AI for researchers and practitioners, but it appears incremental as it builds on existing constraint-based algorithms.
The paper tackled the challenge of causal discovery with categorical variables by proposing a dual-encoding approach, which identified causal structures on the Titanic dataset that aligned with established explainable methods.
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.