  Causal discovery is a fundamental problem in statistics and has wide
applications in different fields. Transfer Entropy (TE) is a important notion
defined for measuring causality, which is essentially conditional Mutual
Information (MI). Copula Entropy (CE) is a theory on measurement of statistical
independence and is equivalent to MI. In this paper, we prove that TE can be
represented with only CE and then propose a non-parametric method for
estimating TE via CE. The proposed method was applied to analyze the Beijing
PM2.5 data in the experiments. Experimental results show that the proposed
method can infer causality relationships from data effectively and hence help
to understand the data better.
