关键词: 因果结构，特征选择，扰动学习，贝叶斯网络，因果强度

Abstract: Causal structure learning is an important causal knowledge discovery method to disclose the nature of causal interactions in the I3ayesian Networks. The causal relations are difficult to be discovered by only using observation data. On the other hand, actually, we are often only interested in local causal structure about a target variable. This paper presented a local causal structure learning method by integrating feature selection into intervention called a local causal structural active learning based on causal power(CSI-LCSL). CSI-LCSL integrated the dividing structure ability of Markov blanket and causal discovery ability of intervention learning. Firstly, under the faithfulness assumption, CSI- LCSL utilized HITON-MI3 algorithm to obtain the Markov blanket of interested variable for generating a local model. I}hen, we selected a intervention variable from the local model by using non-sys entropy to generate interventional data by perfect experiments. Finally,we used an exact method algorithm to obtain a local causal structure of the interested variable by combining observational data and interventional data. A series of comparative experiments on two standard 13aycsian networks show that our method has excellent learning accuracy.

Key words: Causal structurc,Fcaturc sclcction,Intcrvcntion lcarning,l3aycsian nctworks,Causal power