Dependence minimizing regression with model selection for non-linear causal inference under non-Gaussian noise (情報論的学習理論と機械学習)

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概要

• 論文の詳細を見る

• The discovery of non-linear causal relationship under additive non-Gaussian noise models has attracted considerable attention recently because of their high flexibility. In this paper, we propose a novel causal inference algorithm called least-squares independence regression (LSIR). LSIR learns the additive noise model through minimization of an estimator of the squared-loss mutual information between inputs and residuals. A notable advantage of LSIR over existing approaches is that tuning parameters such as the kernel width and the regularization parameter can be naturally optimized by cross-validation, allowing us to avoid overfitting in a data-dependent fashion. Through experiments with real-world datasets, we show that LSIR compares favorably with the state-of-the-art causal inference method.

• 社団法人電子情報通信学会の論文
• 2010-06-07

著者

• Yamada Makoto

  Department Of Chemistry Faculty Of Science Okayama University Of Science

• Sugiyama Masashi

  Department Of Computer Science Tokyo Institute Of Technology

• YAMADA Makoto

  Tokyo Institute of Technology

• Yamada Makoto

  Tokyo Inst. Of Technol.

• Yamada Makoto

  Department Of Chemistry And Biomolecular Science Toho University

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

• Sugiyama Masashi

  Department of Applied Chemistry, Yamanashi University

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