Abstract
To examine the structural learning of the additive noise model in causal discovery, a new algorithm RCS (Rank-Correlation-Statistics) is proposed in combination with the rank correlation method. This algorithm can effectively process the multivariate linear Non-Gaussian data, and multivariate nonlinear non-Gaussian data. In this article, combined with hypothesis testing, a constraint method is proposed to select the potential neighbors of the target node, which greatly reduces the search space and obtains good time performance. Then the method is compared with the existing technology on 7 networks on the additive noise structure model. The results show that the RCS algorithm is superior to existing algorithms in terms of accuracy and time performance. Finally, it shows that the RCS algorithm has a good application on real data.
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Yang, J., Fan, G., Xie, K., Chen, Q., Wang, A. (2021). Additive Noise Model Structure Learning Based on Rank Statistics. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_11
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