Estimating Transfer Entropy via Copula Entropy
arXiv:1910.04375
Abstract
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.
17 pages, 5 figures. with new experiments, discussion, and section on related research
References in corpus (8)
- Brownian distance covariance
- Kernel-based Conditional Independence Test and Application in Causal Discovery
- Nonparametric testing of conditional independence by means of the partial copula
- Testing Conditional Independence via Quantile Regression Based Partial Copulas
- Discovering Association with Copula Entropy
- copent: Estimating Copula Entropy and Transfer Entropy in R
- Causal Compression
- Conditional independence testing via weighted partial copulas and nearest neighbors