Finding causal knowledge based on Bayesian network methods

At present, the methods of learning Bayesian network are not fit for finding causal knowledge from data, or require causal order between variables. While in reality often there is no prior knowledge of variable causal order. In this paper, an effective and practical method of learning causal Bayesian network is presented to find causal knowledge from data. Firstly, a maximal likelihood tree is built from data. Then a causal tree is obtained by orienting the edges of the maximal likelihood tree. Finally, a causal Bayesian network can be established based on local search & scoring method by finding father nodes of a node.