  The machine learning community has recently devoted much attention to the
problem of inferring causal relationships from statistical data. Most of this
work has focused on uncovering connections among scalar random variables. We
generalize existing methods to apply to collections of multi-dimensional random
vectors, focusing on techniques applicable to linear models. The performance of
the resulting algorithms is evaluated and compared in simulations, which show
that our methods can, in many cases, provide useful information on causal
relationships even for relatively small sample sizes.
