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2008

Discovering Cyclic Causal Models by Independent Components Analysis

14 years 27 days ago
Discovering Cyclic Causal Models by Independent Components Analysis
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". 1 Linear SEMs Linear structural equation models (SEMs) are statistical causal models widely used in the natural and social sciences (including econometrics, political science, sociology, and biology) [1]. The variables in a linear SEM can be divided into two sets, the error terms (typically unobserved), and the...
Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Pat
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where UAI
Authors Gustavo Lacerda, Peter Spirtes, Joseph Ramsey, Patrik O. Hoyer
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