Uncertainty in Artificial Intelligence
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Discovering Cyclic Causal Models by Independent Components Analysis
Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik Hoyer
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'.
Pages: 366-374
PS Link:
PDF Link: /papers/08/p366-lacerda.pdf
AUTHOR = "Gustavo Lacerda and Peter Spirtes and Joseph Ramsey and Patrik Hoyer",
TITLE = "Discovering Cyclic Causal Models by Independent Components Analysis",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "366--374"

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