关键词: 隐变量, 马尔科夫毯, 扰动学习, 因果关系分析, 因果结构熵

Abstract: Hidden variable discovering algorithm of structural analysis is difficult to discover hidden variables effectively and possesses?poor interpretability. Based on the causality and the uncertainty of local structure, this paper presents a hidden variable discovering algorithm based on local causality analysis (LCAHD). LCAHD algorithm introduces the definition of causal structure entropy, which integrates causal knowledge and uncertainty knowledge, regards the uncertainty of causality as the judgment of the existence of hidden variables, and proves the judgment theoretically. Firstly, LCAHD algorithm obtains the Markov blanket of interested variable to extract the local dependency structure, then utilizes interventional to generate interventional data, and joints interventional data and observational data to study local causality in the local dependency structure. Secondly, it utilizes causal structure entropy to measure the uncertainty of causality in the local causal structure, and utilizes the judging criteria of hidden variables and the uncertainty of causality to determine the existence of hidden variables. This paper carries on experiments on the standard network and stock network respectively. The experimental results show that this algorithm can effectively determine the location of hidden variables with strong interpretability.

Key words: hidden variables, Markov blanket, intervention learning, causality analysis, causal structure entropy