A Causal Argumentation Method for Explainability of Machine Learning Models

2026-05-23 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

A novel method integrates causality with argument-based reasoning to enhance the explainability of machine learning models, addressing the limitation of traditional XAI methods that often fail to clarify *why* predictions are made. The approach identifies causal relationships among variables using constraint-based causal discovery, then translates these into a Bipolar Argumentation Framework (BAF) to represent supportive and opposing feature interactions. By applying semi-stable semantics, the method finds extensions of features that explain specific outcomes. Demonstrated on the Titanic and Pima Diabetes benchmark datasets, the causal-argumentation framework's explanations align with feature importance scores from standard post-hoc techniques like SHAP, LIME, and pruned decision trees, providing interpretable, structurally grounded insights into feature interactions.

Key takeaway

For AI Scientists and Machine Learning Engineers seeking deeper model interpretability, this causal-argumentation method offers a robust way to explain *why* predictions occur, beyond mere feature relevance. You should consider integrating this framework to generate structurally grounded, human-readable explanations that highlight feature interactions and their causal influence. This can improve trust and transparency in critical applications, especially when validating against established baselines like SHAP and decision trees.

Key insights

Integrating causal discovery with argumentation frameworks provides "why" explanations for ML model predictions.

Principles

• Causal understanding requires identifying features that, if changed, alter outcomes.
• Human reasoning is inherently argumentative, weighing supporting and opposing evidence.
• Semi-stable semantics maximize the range of coherently decided arguments for comprehensive explanations.

Method

The method involves data preparation (entropy-based binning, dual-run encoding), causal discovery (FCI algorithm), unified graph construction, and BAF creation with probabilistic reasoning under semi-stable semantics.

In practice

• Discretize numerical features using supervised entropy-based binning.
• Employ a dual-run causal discovery strategy with drop-first and drop-last encodings.
• Interpret semi-stable extensions as coherent, dialectically structured explanations for outcomes.

Topics

• Explainable AI
• Causal Discovery
• Argumentation Frameworks
• Machine Learning Interpretability
• Semi-Stable Semantics
• FCI Algorithm

Code references

• eigenhenry/causal_argumentation

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.