  Causal discovery automates the learning of causal Bayesian networks from data
and has been of active interest from their beginning. With the sourcing of
large data sets off the internet, interest in scaling up to very large data
sets has grown. One approach to this is to parallelize search using Markov
Blanket (MB) discovery as a first step, followed by a process of combining MBs
in a global causal model. We develop and explore three new methods of MB
discovery using Minimum Message Length (MML) and compare them empirically to
the best existing methods, whether developed specifically as MB discovery or as
feature selection. Our best MML method is consistently competitive and has some
advantageous features.
