Natural XAI: generating feasible, actionable, and causally-aware counterfactual explanations in natural language.

Counterfactual explanations have become a significant component in eXplainable AI (XAI), offering intuitive "what if" scenarios. However, typical numeric or tabular outputs can be vague to non-technical audiences. Additionally, many counterfactual methods ignore causal relationships or suggest inactionable changes such as "be younger by five years," raising concerns over realism and ethics. To address these issues, we propose a holistic approach that integrates a Feature Actionability Taxonomy (FAT) and causal discovery into counterfactual generation, thereby ensuring realistic, ethically sound, and semantically transparent explanations in natural language. We further introduce an interactive, agentic workflow enabling users to iteratively refine constraints. Through extensive user studies, pilot evaluations, and synergy with Case-Based Reasoning (CBR), this approach yields explanations that are accessible, trust-enhancing, and practically useful in domains such as healthcare, finance, and education.