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ICML
2007
IEEE

A kernel-based causal learning algorithm

10 years 5 days ago
A kernel-based causal learning algorithm
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.
Xiaohai Sun, Dominik Janzing, Bernhard Schölk
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Xiaohai Sun, Dominik Janzing, Bernhard Schölkopf, Kenji Fukumizu
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