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


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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University of Melbourne Researchers

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