The causal autoregressive flow model has realized promising results on the causal direction inference problem when the noise is affected by parent nodes. However, to date, existing methods suffer from low accuracy and high computational cost due to the global structure search. Therefore, in this study, a two-stage causal structure learning algorithm is designed for non-temporal observation data. The first stage involves obtaining the basic causal skeleton based on the conditional independence of the observed data from a completely undirected graph, and the second stage involves inferring causal direction by using normalizing flow to compare the edge likelihood probability in different directions based on the causal autoregressive flow model. The experiments on the simulated data shows that the proposed algorithm outperforms the existing mainstream causal structure learning algorithm, and the F1 score of the proposed algorithm is 15%-28% higher than the baseline methods. Similarly on the real world data, when compared with the mainstream causal learning algorithms, the proposed algorithm can learn the causal relationship more completely and accurately, and the F1 score of the proposed algorithm is 28%-48% higher than the baseline methods. Experimental results demonstrate the stronger robustness of the proposed algorithm
