Estimation of linear, non-gaussian causal models in the presence of confounding latent variables
<|reference_start|>Estimation of linear, non-gaussian causal models in the presence of confounding latent variables: The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous work has, however, made an explicit or implicit assumption of gaussianity, limiting the identifiability of the models. We have recently shown (Shimizu et al, 2005; Hoyer et al, 2006) that for non-gaussian distributions the full causal model can be estimated in the no hidden variables case. In this contribution, we discuss the estimation of the model when confounding latent variables are present. Although in this case uniqueness is no longer guaranteed, there is at most a finite set of models which can fit the data. We develop an algorithm for estimating this set, and describe numerical simulations which confirm the theoretical arguments and demonstrate the practical viability of the approach. Full Matlab code is provided for all simulations.<|reference_end|>
@article{hoyer2006estimation,
    title={Estimation of linear, non-gaussian causal models in the presence of
  confounding latent variables},
    author={Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen},
    journal={arXiv preprint arXiv:cs/0603038},
    year={2006},
    archivePrefix={arXiv},
    eprint={cs/0603038},
    primaryClass={cs.AI}
}