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Learning causality by identifying common effects with kernel-based dependence measures

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84243

Sun,  X
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75626

Janzing,  D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Sun, X., & Janzing, D. (2007). Learning causality by identifying common effects with kernel-based dependence measures. Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), 453-458.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CE21-7
Abstract
We describe a method for causal inference that measures the strength of statistical dependence by the Hilbert-Schmidt norm of kernel-based conditional cross-covariance operators. We consider the increase of the dependence of two variables X and Y by conditioning on a third variable Z as a hint for Z being a common effect of X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges according to the majority vote. For most of our experiments with artificial and real-world data our method has outperformed the conventional constraint-based inductive causation (IC) algorithm.