A simple method for reverse engineering causal networks

We present a simple method for 'reverse engineering' causal networks, based on mutual information, as a correlation measure. The goal of our method is not to recover all the causal interactions in a network but rather to recover some causal interactions with a very high confidence. For this purpose, we derive an 'exact' theoretical result for the statistical significance of mutual information. Also, we give some numerical simulation results, obtained for random Boolean networks, as an idealized model of genetic regulatory networks.