# Estimating Transfer Entropy via Copula Entropy

arXiv: https://arxiv.org/abs/1910.04375 · HTML: https://mlsanity.com/paper/1910.04375/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [TREET: TRansfer Entropy Estimation via Transformers](https://mlsanity.com/paper/2402.06919/) (2024)
- [Identifying Time Lag in Dynamical Systems with Copula Entropy based Transfer Entropy](https://mlsanity.com/paper/2301.06037/) (2023)
- [Causal Domain Adaptation with Copula Entropy based Conditional Independence Test](https://mlsanity.com/paper/2202.13482/) (2022)
- [Evaluating Independence and Conditional Independence Measures](https://mlsanity.com/paper/2205.07253/) (2022)
- [System Identification with Copula Entropy](https://mlsanity.com/paper/2304.12922/) (2023)
- [Root Cause Analysis on Energy Efficiency with Transfer Entropy Flow](https://mlsanity.com/paper/2401.05664/) (2024)

## Abstract (arXiv)

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.