A linear non-gaussian acyclic model for causal discovery

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A linear non-gaussian acyclic model for causal discovery (2006)

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by Shohei Shimizu , Patrik O. Hoyer , Aapo Hyvärinen , Antti Kerminen

Venue:	J. Machine Learning Research
Citations:	103 - 30 self

BibTeX

@ARTICLE{Shimizu06alinear,
    author = {Shohei Shimizu and Patrik O. Hoyer and Aapo Hyvärinen and Antti Kerminen},
    title = {A linear non-gaussian acyclic model for causal discovery},
    journal = {J. Machine Learning Research},
    year = {2006},
    pages = {2003--2030}
}

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Abstract

In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data. 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, 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 and real-world data.

Keyphrases

linear non-gaussian acyclic model causal discovery pre-specified time-ordering data generating process unobserved confounders recent year non-gaussian distribution real-world data lingam analysis independent component analysis statistical method complete matlab package non-experimental data disturbance variable causal structure non-zero variance several method various assumption continuous-valued data purely observational data complete causal structure

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