Mining Causality from Non-categorical Numerical Data

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Causality can be detectable from categorical data: hot weather causes dehydration, smoking causes cough, etc. However, in the context of numerical data, most of the times causality is difficult to detect and measure. In fact, considering two time series, although it is possible to measure the correlation between both associated variables, correlation metrics don’t show the cause-effect direction and then, cause and effect variables are not identified by those metrics.In order to detect possible cause-effect relationships as well as measuring the strength of causality from non-categorical numerical data, this paper presents an approach which is a simple and efficient alternative to other methods based on regression models.
Original languageUnknown
Title of host publicationBehavior Computing. Analysis, Mining and Decision
Place of PublicationLondon
PublisherSpringer London
Pages215-227
ISBN (Print)978-1-4471-2968-4
Publication statusPublished - 1 Jan 2012

Cite this

Lopes, J. G. P., & Silva, J. F. F. (2012). Mining Causality from Non-categorical Numerical Data. In Behavior Computing. Analysis, Mining and Decision (pp. 215-227). London: Springer London.