Abstract: Machine learning is one of the important technical means to realize artificial intelligence, and it has important applications in the fields of computer vision, natural language processing, search engines and recommendation systems. Existing machine learning methods often focus on the correlations in the data and ignore the causality. With the increase in application requirements, their drawbacks have gradually begun to appear, facing a series of urgent problems in terms of interpretability, transferability, robustness, and fairness. In order to solve these problems, researchers have begun to re-examine the necessity of modeling causal relationship, and related methods have become one of the recent research hotspots. We organize and summarize the work of applying causal techniques and ideas to solve practical problems in the field of machine learning in recent years, and sort out the development venation of this emerging research direction. First, we briefly introduce the closely related causal theory to machine learning. Then, we classify and introduce each work based on the needs of different problems in machine learning, explain their differences and connections from the perspective of solution ideas and technical means. Finally, we summarize the current situation of causal machine learning, and make predictions and prospects for future development trends.