Causal Discovery by Randomness Test
Publication Type:
Refereed Conference Meeting Proceeding
Abstract:
Probabilistic methods for causal discovery are based on the detection
of patterns of correlation between variables. They are based on
statistical theory and have revolutionised the study of causality.
However, when correlation itself is unreliable, so are probabilistic
methods: nonsense correlations can lead to spurious causal links,
while nonmonotonic functional relationships between variables can
prevent the detection of causal links. We describe a new heuristic
method for inferring causality between two continuous or integer
variables, based on a nonparametric randomness test. We evaluate the
accuracy of the method by comparing it to published algorithms on real
and artificial datasets, and show that it largely avoids these false
positives and negatives.
Conference Name:
14th International Symposium on Artificial Intelligence and Mathematics
Digital Object Identifer (DOI):
10.NA
Publication Date:
14/01/2016
Conference Location:
United States of America
Research Group:
Institution:
National University of Ireland, Cork (UCC)
Open access repository:
Yes
Publication document: