Estimation of linear non-Gaussian acyclic models for latent factors
By Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen. 2009
62 citations recorded by the source corpus.
Many methods have been proposed for discovery of causal relations among observed variables. But one often wants to discover causal relations among latent factors rather than observed variables. Some methods have been proposed to estimate linear acyclic models for latent factors that are measured by observed variables. However, most of the methods use data covariance structure alone for model identification, and this leads to a number of indistinguishable models. In this paper, we show that a linear acyclic model for latent factors is identifiable when the data are non-Gaussian.
Subfields: Analytical Chemistry, Artificial Intelligence, Signal Processing
How researchers see this paper
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In Analytical Chemistry
- Kun Zhang
- Clark Glymour
- Peter Spirtes
- Jan Peters
- J. Ramsey
- Bernhard Schölkopf
- Mingming Gong
- Dominik Janzing
In Artificial Intelligence
- Bernhard Schölkopf
- Jan Peters
- Kun Zhang
- Dominik Janzing
- David Lopez-Paz
- Peter Spirtes
- Clark Glymour
- Joris M. Mooij
In Signal Processing
- Bernhard Schölkopf
- Dominik Janzing
- Jan Peters
- Kun Zhang
- Clark Glymour
- David Lopez-Paz
- Peter Spirtes
- Oliver Stegle
Citations in the corpus
- Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders (2013, methodology): “Under the assumption of linear relationships between variables and non-Gaussian distributions, confounders may be identified using Independent Component Analysis (Shimizu et al., 2009).”
- Detecting low-complexity unobserved causes (2011, methodology): “Known methods include linear relations between real-valued non-Gaussian variables (e.g. [Shimizu et al., 2009]) and non-linear relations with additive noise [Janzing et al.].”
- Causal Discovery with Linear Non-Gaussian Models under Measurement Error: Structural Identifiability Results (2018, methodology): “As a consequence, unfortunately, the above causal analysis methods based on LiNGAM, including ICA-LiNGAM and Direct-LiNGAM, do not apply.”