关键词: 灵敏性分析, 扰动学习, 因果网络, 最大似然估计(MLE)

Abstract: Learning causal network by combining observational data and intervention data is a machine learning method based on intervention, and the intervention learning can discover causal relationships of network from small samples. The influences of disturbance for causality mainly embody in network parameters. This paper presents an interventional learning algorithm on causal network parameters based on sensitivity analysis (ILPSA). For a known prior network, ILPSA algorithm uses junction tree inference algorithm to produce the sensitivity function, and proposes the active selection method of intervention nodes by analyzing the parameter importance of sensitivity function. Further, it manipulates the intervention nodes to produce the intervention data, combines observational data and intervention data, learns the parameters of causal network by maximum likelihood estimation (MLE) method, and measures the learning results by KL divergence. The results of algorithm comparison and experiment show that ILPSA algorithm is better than the methods of random intervention and no intervention, especially, the ILPSA algorithm is more effective in the smaller samples.

Key words: sensitivity analysis, intervention learning, causal network, maximum likelihood estimation (MLE)