Causal Interpretability

Causal interpretability refers to the ability to understand and explain the cause-and-effect relationships within a model's predictions. It focuses on identifying how changes in input variables directly influence outcomes, allowing for clearer insights into the underlying mechanisms driving the model's behavior. This approach enhances transparency and trust in AI systems by providing a more intuitive understanding of their decision-making processes.