A Causal Argumentation Method for Explainability of Machine Learning Models
Authors: Henry Salgado, Meagan R. Kendall, Martine Ceberio
Summary
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.
Relevance
Read next because A Causal Argumentation Method for Explainability of Machine Learning Models overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe", experiment "#351 follow-up: broader-vocab position-0 sweep at T=1.0 + position-1 suffix isolation". Matching terms: rate, compare, model. Source: arxiv cs.AI (Artificial Intelligence).
Threat model
Potential threat/caveat for clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)": this item discusses benchmark.
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 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 datasets and compare its results against standard post-hoc explainability approaches.