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A new causal discovery heuristic. (English) Zbl 1395.68239
Summary: 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: unusual data 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 variables, based on randomness and unimodality tests and making few assumptions about the data. We evaluate the method against probabilistic and additive noise algorithms on real and artificial datasets, and show that it performs competitively.
MSC:
68T05 Learning and adaptive systems in artificial intelligence
62A01 Foundations and philosophical topics in statistics
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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