@techreport{oai:ipsj.ixsq.nii.ac.jp:00218599,
 author = {張, 秉元 and 鈴木, 讓 and Bingyuan, Zhang and Joe, Suzuki},
 issue = {29},
 month = {Jun},
 note = {Conditional Independence (CI) testing is a fundamental problem in statistics, which is applied directly to causal　discovery. Many nonparametric CI tests are developed, but a common challenge exists: current methods perform poorly with a high dimensional conditioning set. To tackle this problem, we consider a novel nonparametric CI test using a kernel-based measure, which can be viewed as an extension of the Hilbert-Schmidt Independence Criterion (HSIC). The experimental results show that our proposed method leads to a significant performance improvement when compared with previous methods. In particular, our method performs well against the growth of the dimension of the conditioning set. Meanwhile, our method shows competitive scalability regarding the sample size ???? and the dimension of the conditioning set., Conditional Independence (CI) testing is a fundamental problem in statistics, which is applied directly to causal　discovery. Many nonparametric CI tests are developed, but a common challenge exists: current methods perform poorly with a high dimensional conditioning set. To tackle this problem, we consider a novel nonparametric CI test using a kernel-based measure, which can be viewed as an extension of the Hilbert-Schmidt Independence Criterion (HSIC). The experimental results show that our proposed method leads to a significant performance improvement when compared with previous methods. In particular, our method performs well against the growth of the dimension of the conditioning set. Meanwhile, our method shows competitive scalability　regarding the sample size n and the dimension of the conditioning set.},
 title = {HSICを拡張した条件付き独立検定},
 year = {2022}
}