A Causal Argumentation Method for Explainability of Machine Learning Models

ArXi:2605.21758v1 Announce Type: new 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 ive and opposing interactions among features.