Minimum Sample Size for Reliable Causal Inference Using Transfer Entropy

Author(s)

Ramos, Antonio M. T. ; Macau, Elbert E. N.

• Document type: Journal article
• Source: Entropy; v. 19, n. 4 APR 2017.
• Web of Science Citations: 1

Abstract

Transfer Entropy has been applied to experimental datasets to unveil causality between variables. In particular, its application to non-stationary systems has posed a great challenge due to restrictions on the sample size. Here, we have investigated the minimum sample size that produces a reliable causal inference. The methodology has been applied to two prototypical models: the linear model autoregressive-moving average and the non-linear logistic map. The relationship between the Transfer Entropy value and the sample size has been systematically examined. Additionally, we have shown the dependence of the reliable sample size and the strength of coupling between the variables. Our methodology offers a realistic lower bound for the sample size to produce a reliable outcome. (AU)

• FAPESP's process: 14/14229-2 - Non-linear and chaotic dynamics with spatial distribution and their characterization by using the complex network approach
• Grantee: Antônio Mário de Torres Ramos
• Support type: Scholarships in Brazil - Post-Doctorate
• FAPESP's process: 15/07373-2 - Development of quantifiers through Information Theory techniques and probabilistic graphical models with application in the Amazon Region
• Grantee: Antônio Mário de Torres Ramos
• Support type: Scholarships abroad - Research Internship - Post-doctor
• FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support type: Research Projects - Thematic Grants