Discovering biomedical causality by a generative Bayesian causal network under uncertainty

With the rapid development of biomedical technology, discovering causality from genes and human physiological and pathological characteristics has become a hot but challenge spot over the past decades. Due to the increment of the amount of biomedical data, discovering causality from observed data becomes more and more difficult to search this large body of knowledge in a meaningful manner. To address the issues in existing causality discovering models, we introduce a generative Bayesian causal network that combines neural network to explicitly characterize these unique causal-effect relationships as a variable number of nodes and links. Particularly, a basic skeleton is generated for node selection to reduce the network size by minimizing the maximum mean discrepancy among variables. In addition, a causal generative neural network model is presented to construct causal network with cause-effect scores between variables. Empirical evaluations on two publicly available biomedical datasets and four synthetic datasets suggest our approach significantly outperforms the state-of-the-art methods in discovering causal relationships among biomedical variables.