Causal Discovery from Changes
  We propose a new method of discovering causal structures, based on the
detection of local, spontaneous changes in the underlying data-generating
model. We analyze the classes of structures that are equivalent relative to a
stream of distributions produced by local changes, and devise algorithms that
output graphical representations of these equivalence classes. We present
experimental results, using simulated data, and examine the errors associated
with detection of changes and recovery of structures.
