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Causal Discovery by Randomness Test

Authors: 

Steven Prestwich, Armagan Tarim, Ibrahim Ozkan

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
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
Yes
Publication document: