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We propose a method to classify the causal relationship between two discretevariables given only the joint distribution of the variables, acknowledgingthat the method is subject to an inherent baseline error. We assume that thecausal system is acyclicity, but we do allow for hidden common causes. Ouralgorithm presupposes that the probability distributions $P(C)$ of a cause $C$is independent from the probability distribution $P(E\mid C)$ of thecause-effect mechanism. While our classifier is trained with a Bayesianassumption of flat hyperpriors, we do not make this assumption about our testdata. This work connects to recent developments on the identifiability ofcausal models over continuous variables under the assumption of "independentmechanisms". Carefully-commented Python notebooks that reproduce all ourexperiments are available online athttp://vision.caltech.edu/~kchalupk/code.html.

Estimating Causal Direction and Confounding of Two Discrete Variables