Estimating Transfer Entropy via Copula Entropy
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Paths to Causality: Finding Informative Subgraphs Within Knowledge Graphs for Knowledge-Based Causal Discovery
Hillclimb-Causal Inference: A Data-Driven Approach to Identify Causal Pathways Among Parental Behaviors, Genetic Risk, and Externalizing Behaviors in Children
REX: Causal Discovery based on Machine Learning and Explainability techniques
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
Causal Discovery in Dynamic Fading Wireless Networks
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery
토론
이 논문과 연결된 포럼 글이 아직 없습니다.