Non-Gaussian Methods for Causal Structure Learning
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Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. 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 using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.
KeywordsCausal structure discovery Observational data Non-Gaussianity Structural causal models
The author thanks the guest editor Wolfgang Wiedermann and two reviewers for their helpful comments.
This work was supported by JSPS KAKENHI Grant Number 16K00045.
Compliance with Ethical Standards
Conflict of Interest
The author declares that there is no conflict of interest.
This article does not contain any studies with human participants or animals performed by the author.
Informed consent was not required for this study.
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