Conference Proceedings
Likelihood-Free Overcomplete ICA and Applications In Causal Discovery
DING Chenwei, Mingming Gong, Kun Zhang, Dacheng Tao
Advances in Neural Information Processing Systems (NeurIPS; CORE Rank A*) | The Neural Information Processing Systems Foundation | Published : 2020
Abstract
Causal discovery witnessed significant progress over the past decades. In particular, many recent causal discovery methods make use of independent, non-Gaussian noise to achieve identifiability of the causal models. Existence of hidden direct common causes, or confounders, generally makes causal discovery more difficult; whenever they are present, the corresponding causal discovery algorithms can be seen as extensions of overcomplete independent component analysis (OICA). However, existing OICA algorithms usually make strong parametric assumptions on the distribution of independent components, which may be violated on real data, leading to sub-optimal or even wrong solutions. In addition, ex..
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Awarded by National Science Foundation
Funding Acknowledgements
Chenwei Ding and Dacheng Tao would like to acknowledge the support by Australian Research Council Projects FL-170100117 and DP-180103424. Kun Zhang would like to acknowledge the support by National Institutes of Health under Contract No. NIH-1R01EB022858-01, FAINR01EB022858, NIH-1R01LM012087, NIH-5U54HG008540-02, and FAIN-U54HG008540, by the United States Air Force under Contract No. FA8650-17-C-7715, and by National Science Foundation EAGER Grant No. IIS-1829681. The National Institutes of Health, the U.S. Air Force, and the National Science Foundation are not responsible for the views reported in this article.