Mining Causality from Non-categorical Numerical Data
Jan 2012
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.
Behavior Computing. Modeling, Analysis, Mining and Decision