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arXiv · 1206.3273

Discovering Cyclic Causal Models by Independent Components Analysis

Gustavo Lacerda·Peter L. Spirtes·Joseph Ramsey·Patrik O. Hoyer
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Abstract

We generalize Shimizu et al's (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data. By relaxing the assumption that the generating SEM's graph is acyclic, we solve the more general problem of linear non-Gaussian (LiNG) SEM discovery. LiNG discovery algorithms output the distribution equivalence class of SEMs which, in the large sample limit, represents the population distribution. We apply a LiNG discovery algorithm to simulated data. Finally, we give sufficient conditions under which only one of the SEMs in the output class is 'stable'.

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Gustavo Lacerda, Peter L. Spirtes, Joseph Ramsey, Patrik O. Hoyer. 2012-06-13. Discovering Cyclic Causal Models by Independent Components Analysis. https://arxiv.org/abs/1206.3273

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