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Discovering Cyclic Causal Models by Independent Components Analysis
This video was recorded at 24th Conference on Uncertainty in Artificial Intelligence (UAI), Helsinki 2008. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
