Causal Structure Learning Algorithm Based on Partial Rank Correlation under Additive Noise Model

Aiming at the structural learning problem of the additive noise model in causal discovery and the challenge of massive data processing in the era of artificial intelligence, this paper combines partial rank correlation coefficients and proposes two new Bayesian network causal structure learning algorithms: PRCB algorithm based on threshold selection and PRCS algorithm based on hypothesis testing. We mainly made three contributions. First, we proved that the partial rank correlation coefficient can be used as the standard of independence test, and explored the distribution of corresponding statistics. Second, the partial rank correlation coefficient is associated with the correlation, and a causal discovery algorithm PRCB based on partial rank correlation and an improved PRCS algorithm based on hypothesis testing are proposed. Finally, comparing with the existing technology on seven classic Bayesian networks, it proves the superiority of the algorithm in low-dimensional networks; the processing of millions of data on three high-dimensional Bayesian networks verifies the high-efficiency performance of the algorithm in high-dimensional large sample data; the application performance of the algorithm is tested by performing fault prediction on the real power plant equipment measurement point data set. Theoretical analysis and experimental results have proved the superiority of the algorithm.