  Causal Bayesian Networks provide an important tool for reasoning under
uncertainty with potential application to many complex causal systems.
Structure learning algorithms that can tell us something about the causal
structure of these systems are becoming increasingly important. In the
literature, the validity of these algorithms is often tested for sensitivity
over varying sample sizes, hyper-parameters, and occasionally objective
functions. In this paper, we show that the order in which the variables are
read from data can have much greater impact on the accuracy of the algorithm
than these factors. Because the variable ordering is arbitrary, any significant
effect it has on learnt graph accuracy is concerning, and this raises questions
about the validity of the results produced by algorithms that are sensitive to,
but have not been assessed against, different variable orderings.
