Work

Fractal Conditional Correlation Dimension Infers Complex Causal Networks

Year: 2024

Type: article

Abstract: Determining causal inference has become popular in physical and engineering applications. While the problem has immense challenges, it provides a way to model the complex networks by observing the time series. In this paper, we present the optimal conditional correlation dimensional geometric information flow principle (oGeoC) that can reveal direct and indirect causal relations in a network through geometric interpretations. We introduce two algorithms that utilize the oGeoC principle to discover the direct links and then remove indirect links. The algorithms are evaluated using coupled logistic networks. The results indicate that when the number of observations is sufficient, the proposed algorithms are highly accurate in identifying direct causal links and have a low false positive rate.

Source: Entropy

DOI: 10.3390/e26121030

Authors: Özge Canlı Usta, Erik M. Bollt

Institutions: Clarkson University, Dokuz Eylül University

Language: English

Topic: Complex Systems and Time Series Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $2,955

Funders: National Institutes of Health, Defense Advanced Research Projects Agency, Office of Naval Research, Army Research Office