An experimental comparison of linear non-Gaussian causal discovery methods and their variants

Yasuhiro Sogawa, Shohei Shimizu, Yoshinobu Kawahara, Takashi Washio

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

6 被引用数 (Scopus)

抄録

Many multivariate Gaussianity-based techniques for identifying causal networks of observed variables have been proposed. These methods have several problems such that they cannot uniquely identify the causal networks without any prior knowledge. To alleviate this problem, a non-Gaussianity-based identification method LiNGAM was proposed. Though the LiNGAM potentially identifies a unique causal network without using any prior knowledge, it needs to properly examine independence assumptions of the causal network and search the correct causal network by using finite observed data points only. On another front, a kernel based independence measure that evaluates the independence more strictly was recently proposed. In addition, some advanced generic search algorithms including beam search have been extensively studied in the past. In this paper, we propose some variants of the LiNGAM method which introduce the kernel based method and the beam search enabling more accurate causal network identification. Furthermore, we experimentally characterize the LiNGAM and its variants in terms of accuracy and robustness of their identification.

本文言語英語
ホスト出版物のタイトル2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOI
出版ステータス出版済み - 12 1 2010
外部発表はい
イベント2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, スペイン
継続期間: 7 18 20107 23 2010

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

会議

会議2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
国/地域スペイン
CityBarcelona
Period7/18/107/23/10

All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 人工知能

引用スタイル