Uncertainty in Artificial Intelligence
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Discovery of non-gaussian linear causal models using ICA
Shohei Shimizu, Aapo Hyvarinen, Yutaka Kano, Patrik Hoyer
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000). Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to discover the complete causal structure of continuous-valued data, under the assumptions that (a) the data generating process is linear, (b) there are no unobserved confounders, and (c) disturbance variables have non-gaussian distributions of non-zero variances. The solution relies on the use of the statistical method known as independent component analysis (ICA), and does not require any pre-specified time-ordering of the variables. We provide a complete Matlab package for performing this LiNGAM analysis (short for Linear Non-Gaussian Acyclic Model), and demonstrate the effectiveness of the method using artificially generated data.
Pages: 525-533
PS Link:
PDF Link: /papers/05/p525-shimizu.pdf
AUTHOR = "Shohei Shimizu and Aapo Hyvarinen and Yutaka Kano and Patrik Hoyer",
TITLE = "Discovery of non-gaussian linear causal models using ICA",
BOOKTITLE = "Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)",
ADDRESS = "Arlington, Virginia",
YEAR = "2005",
PAGES = "525--533"

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