A Causal Argumentation Method for Explainability of Machine Learning Models

📰 ArXiv cs.AI

Learn to explain machine learning model predictions using causal argumentation, enhancing model transparency and trustworthiness

advanced Published 23 May 2026

Action Steps

Identify causal relationships among variables using causal discovery methods
Translate causal relationships into a Bipolar Argumentation Frame
Apply argument-based reasoning to explain model predictions
Evaluate the effectiveness of the causal argumentation method in explaining model decisions
Refine the method based on evaluation results to improve model explainability

Who Needs to Know This

Data scientists and machine learning engineers can benefit from this method to provide more insightful explanations of their models' decisions, improving model interpretability and reliability

Key Insight

💡 Causal argumentation can provide more nuanced explanations of machine learning model predictions by integrating causality with argument-based reasoning

Key Takeaways

Learn to explain machine learning model predictions using causal argumentation, enhancing model transparency and trustworthiness

Full Article

Title: A Causal Argumentation Method for Explainability of Machine Learning Models

Abstract:
arXiv:2605.21758v1 Announce Type: new Abstract: 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 Frame

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