  Statistical machine learning algorithms have achieved state-of-the-art
results on benchmark datasets, outperforming humans in many tasks. However, the
out-of-distribution data and confounder, which have an unpredictable causal
relationship, significantly degrade the performance of the existing models.
Causal Representation Learning (CRL) has recently been a promising direction to
address the causal relationship problem in vision understanding. This survey
presents recent advances in CRL in vision. Firstly, we introduce the basic
concept of causal inference. Secondly, we analyze the CRL theoretical work,
especially in invariant risk minimization, and the practical work in feature
understanding and transfer learning. Finally, we propose a future research
direction in medical image analysis and CRL general theory.
