Causality Learning: A New Perspective for Interpretable Machine Learning
This addresses the need for more interpretable models in fields like image recognition and credit scoring, but it is incremental as it reviews existing methods rather than introducing new ones.
The paper tackles the problem of interpretability in machine learning by shifting focus from association to causality, providing an overview of causal analysis and summarizing recent causal approaches for interpretable machine learning.
Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc. In spite of their great performance in different sectors, researchers still concern about the mechanism under any machine learning (ML) techniques that are inherently black-box and becoming more complex to achieve higher accuracy. Therefore, interpreting machine learning model is currently a mainstream topic in the research community. However, the traditional interpretable machine learning focuses on the association instead of the causality. This paper provides an overview of causal analysis with the fundamental background and key concepts, and then summarizes most recent causal approaches for interpretable machine learning. The evaluation techniques for assessing method quality, and open problems in causal interpretability are also discussed in this paper.