Application of continuous Bayesian network model in cross-sectional survey data
Yang Jin · 2014
This study is to verify that the continuous Bayesian Network model can be used to discover causal information from cross-sectional survey data. Using the L1 MB algorithm, TC, PCB and Two-Phase algorithm, this paper analyzes causal relations in the real data from the National Health and Nutrition Examination Survey. Experimental results show that these algorithms can discover causal relations to various degrees. The PCB algorithm and Two-Phase algorithm that apply to Gaussian or non-Gaussian data outperform the L1 MB algorithm and TC algorithm that only apply to Gaussian data. Combining the PCB algorithm and Two-Phase algorithm for causal analysis, the causal structure thus obtained is more comprehensive.