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Identifying causal relations among multi-variate time series is one of themost important elements towards understanding the complex mechanisms underlyingthe dynamic system. It provides critical tools for forecasting, simulations andinterventions in science and business analytics. In this paper, we proposed agraph neural network approach with score-based method aiming at learning asparse DAG that captures the causal dependencies in a discretized time temporalgraph. We demonstrate methods with graph neural network significantlyoutperformed other state-of-the-art methods with dynamic bayesian networkinginference. In addition, from the experiments, the structural causal model canbe more accurate than a linear SCM discovered by the methods such as Notears.

GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network