Abstract: Microbial communities interact in a complicated way, which can have a detrimental effect on the host’s health either individually or collaboratively. By employing causal inference methods to analyze the characteristics of the relationship between the microbiota and host observation data, the mechanism of action of microorganisms on host health can be inferred, which will help to reduce the possibility of causing unfavorable health consequences. This article reviews recent progress in the application of causal inference in the field of microbiological research, introduces the conception and development of causality, describes the paths of causal inference, modeling and interpretation, and summarizes the causal inference methods and their applications in various fields especially in the microbiological field. However, the mechanism of the causality between microbial communities and their external environments is not yet understood fully. Applying causal inference methods in research on microbial communities will be a hot topic in the future. Thus, the theory and methods of causal inference need be further refined to elucidate the interaction mechanism of microbial communities and their causality with human health.

Key words: microbial communities; human health; causal inference; risk assessment; machine learning