Professor in the School of Mathematical Sciences Published in the Journal Nature Communications

Recently, Professor Ma Huanfei from the School of Mathematical Sciences, in collaboration with colleagues from the University of Chinese Academy of Sciences, Fudan University and the university of Tokyo, proposed a new algorithm for data-driven causal network identification. The achievements of the study entitled "Partial cross mapping eliminates indirect causal influences", was published online in Nature Communications on May 26.


Causality is the most common and fundamental connection among natural phenomenon. In physics, life science, geography and other fields of natural science, as well as in philosophy, economics and other social sciences, it is of great significance to discover the inherent causal relationship and causal network, which can reflect the core interaction mechanism of system evolution. Therefore, how to accurately identify causal relationship and network based on large data scale has become the focus of scientific research and widely concerned by scholars.


This study further improves the existing theoretical system of causal analysis, and provides effective mathematical methods for multi-disciplinary common problems. It has a broad application prospect and embodies the value of applied mathematical research.

 

 

 https://doi.org/10.1038/s41467-020-16238-0

 




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