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We discuss the problem of estimating the causal direction between two observed variables in the presence of hidden common causes. Managing hidden common causes is essential when studying causal relations based on observational data. We previously proposed a Bayesian estimation method for estimating the causal direction using the non-Gaussianity of data. This method does not require us to explicitly model hidden common causes. The experiments on artificial data presented in this paper imply that Bayes factors could be useful for selecting a better causal direction when using a non-Gaussian method.

A Non-Gaussian Approach for Causal Discovery in the Presence of Hidden Common Causes