Causal Structure Learning: a Bayesian approach based on random graphs
  A Random Graph is a random object which take its values in the space of
graphs. We take advantage of the expressibility of graphs in order to model the
uncertainty about the existence of causal relationships within a given set of
variables. We adopt a Bayesian point of view in order to capture a causal
structure via interaction and learning with a causal environment. We test our
method over two different scenarios, and the experiments mainly confirm that
our technique can learn a causal structure. Furthermore, the experiments and
results presented for the first test scenario demonstrate the usefulness of our
method to learn a causal structure as well as the optimal action. On the other
hand the second experiment, shows that our proposal manages to learn the
underlying causal structure of several tasks with different sizes and different
causal structures.
