A generalized prediction framework for granger causality

Christopher J. Quinn, Todd P. Coleman, Negar Kiyavash

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In his 1969 paper, Granger proposed a statistical definition of causality between stochastic processes. It is based on whether causal side information helps in a sequential prediction task. However, his formulation was limited to linear predictors. We describe a generalized framework, where predictions are beliefs and compare the best predictor with side information to the best predictor without side information. The difference in the prediction performance, i.e., regret of such predictors, is used as a measure of causal influence of the side information. Specifically when log loss is used to quantify each predictor's loss and an expectation over the outcomes is used to quantify the regret, we show that the directed information, an information theoretic quantity, quantifies Granger causality. We also explore a more pessimistic setup perhaps better suited for adversarial settings where minimax criterion is used to quantify the regret.

Original languageEnglish (US)
Title of host publication2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011
Pages906-911
Number of pages6
DOIs
StatePublished - Jul 26 2011
Event2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011 - Shanghai, China
Duration: Apr 10 2011Apr 15 2011

Publication series

Name2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011

Other

Other2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011
CountryChina
CityShanghai
Period4/10/114/15/11

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Communication

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