Estimating Transfer Entropy via Copula Entropy
This work addresses causal discovery for statistics and applied fields, but appears incremental as it builds on existing theories of Copula Entropy.
The authors tackled the problem of causal discovery by proving that Transfer Entropy can be represented using Copula Entropy and proposed a non-parametric estimation method, which was applied to Beijing PM2.5 data to effectively infer causality relationships.
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.