Explanation Trees for Causal Bayesian Networks
  Bayesian networks can be used to extract explanations about the observed
state of a subset of variables. In this paper, we explicate the desiderata of
an explanation and confront them with the concept of explanation proposed by
existing methods. The necessity of taking into account causal approaches when a
causal graph is available is discussed. We then introduce causal explanation
trees, based on the construction of explanation trees using the measure of
causal information ow (Ay and Polani, 2006). This approach is compared to
several other methods on known networks.
