PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Assessing Granger Non-Causality Using Nonparametric Measure of Conditional Independence
Sohan Seth and Jose Principe
IEEE Transactions on Neural Networks and Learning Systems Volume 23, Number 1, pp. 47-59, 2012. ISSN 2162-237X


In recent years, Granger causality has become a popular method in a variety of research areas including engineering, neuroscience, and economics. However, despite its simplicity and wide applicability, the linear Granger causality is an insufficient tool for analyzing exotic stochastic processes such as processes involving non-linear dynamics or processes involving causality in higher order statistics. In order to analyze such processes more reliably, a different approach toward Granger causality has become increasingly popular. This new approach employs conditional independence as a tool to discover Granger non-causality without any assumption on the underlying stochastic process. This paper discusses the concept of discovering Granger non-causality using measures of conditional independence, and proposes a novel measure of conditional independence. In brief, the proposed approach estimates the conditional distribution function through a kernel based least square regression approach. This paper also explores the strengths and weaknesses of the proposed method compared to other available methods, and provides a detailed comparison of these methods using a variety of synthetic data sets.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:9064
Deposited By:Sohan Seth
Deposited On:21 February 2012