Mining causality from non-categorical numerical data

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 re-gression models.