Causal Abstraction in Model Interpretability: A Compact Survey
It addresses the problem of making AI models more interpretable for researchers and practitioners, but it is incremental as it synthesizes existing work without introducing new methods or data.
This survey paper explores causal abstraction as a theoretical framework for understanding and explaining the causal mechanisms in complex AI models like deep learning systems, reviewing its foundations, applications, and implications for model interpretability.
The pursuit of interpretable artificial intelligence has led to significant advancements in the development of methods that aim to explain the decision-making processes of complex models, such as deep learning systems. Among these methods, causal abstraction stands out as a theoretical framework that provides a principled approach to understanding and explaining the causal mechanisms underlying model behavior. This survey paper delves into the realm of causal abstraction, examining its theoretical foundations, practical applications, and implications for the field of model interpretability.