• Corpus ID: 221516480

Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis

@article{Harada2020EstimationOS,
  title={Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysis},
  author={Kazuharu Harada and Hironori Fujisawa},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.03077}
}
We consider the problem of inferring the causal structure from observational data, especially when the structure is sparse. This type of problem is usually formulated as an inference of a directed acyclic graph (DAG) model. The linear non-Gaussian acyclic model (LiNGAM) is one of the most successful DAG models, and various estimation methods have been developed. However, existing methods are not efficient for some reasons: (i) the sparse structure is not always incorporated in causal order… 
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