PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A kernel-based causal learning algorithm
X. Sun, D. Janzing and B. Schölkopf
In: Proceedings of the 24th Annual International Conference on Machine Learning(2007).

Abstract

We describe a causal learning method, which employs measuring the strength of statistical dependences in terms of the Hilbert-Schmidt norm of kernel-based cross-covariance operators. Following the line of the common faithfulness assumption of constraint-based causal learning, our approach assumes that a variable Z is likely to be a common effect of X and Y, if conditioning on Z increases the dependence between X and Y. Based on this assumption, we collect "votes" for hypothetical causal directions and orient the edges by the majority principle. In most experiments with known causal structures, our method provided plausible results and outperformed the conventional constraint-based PC algorithm.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4028
Deposited By:Bernhard Schölkopf
Deposited On:25 February 2008