LiNGAM: Non-Gaussian methods for estimating causal structures

  • Shimizu S
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Abstract

In many empirical sciences, the causal mechanisms underlying various phenomena need to be studied. Structural equation modeling is a general framework used for multivariate analysis, and provides a powerful method for studying causal mechanisms. However, in many cases, classical structural equation modeling is not capable of estimating the causal directions of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure of data. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for utilizing the non-Gaussian structure of data and estimating the causal directions of variables. In this paper, we provide an overview of such recent developments in causal inference, and focus in particular on the non-Gaussian methods known as LiNGAM. (PsycINFO Database Record (c) 2014 APA, all rights reserved) (journal abstract)

Author-supplied keywords

  • causal inference
  • causal structure learning
  • estimation of causal directions
  • non-gaussianity
  • struc-
  • tural equation models

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Authors

  • Shohei Shimizu

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