Learning a causal structure: a Bayesian Random Graph approach

Random Graphs are random objects which take its values in the space of graphs. We take advantage of the expresibility 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 random environment. We test our method on a simple scenario, and the experiment confirms that our technique can learn a causal structure. Furthermore, the experiment presented demonstrate the usefulness of our method to learn an optimal action.