  Additive noise models are a class of causal models in which each variable is
defined as a function of its causes plus independent noise. In such models, the
ordering of variables by marginal variances may be indicative of the causal
order. We introduce varsortability as a measure of agreement between the
ordering by marginal variance and the causal order. We show how varsortability
dominates the performance of continuous structure learning algorithms on
synthetic data. On real-world data, varsortability is an implausible and
untestable assumption and we find no indication of high varsortability. We aim
to raise awareness that varsortability easily occurs in simulated additive
noise models. We provide a baseline method that explicitly exploits
varsortability and advocate reporting varsortability in benchmarking data.
