Interaction Information for Causal Inference: The Case of Directed
  Triangle
  Interaction information is one of the multivariate generalizations of mutual
information, which expresses the amount information shared among a set of
variables, beyond the information, which is shared in any proper subset of
those variables. Unlike (conditional) mutual information, which is always
non-negative, interaction information can be negative. We utilize this property
to find the direction of causal influences among variables in a triangle
topology under some mild assumptions.
