  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.
