A Causal Argumentation Method for Explainability of Machine Learning Models
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 datase...
| Authors | Henry Salgado, Meagan R. Kendall, Martine Ceberio |
|---|---|
| Primary category | cs.AI |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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