IntroductionTo ensure the safe and trustworthy deployment of NLP models, it is essential to provide explanations that reflect the true reasoning behind a model’s predictions. Traditional explanation methods often rely on correlation rather than causation, which can result in misleading interpretations. This paper proposes two model-agnostic approaches for generating faithful explanations using c..
