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.
[ Paper ]