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Statistical causal discovery = LiNGA...

Shimizu, Shohei.

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Statistical causal discovery = LiNGAM approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical causal discovery/ by Shohei Shimizu.
其他題名:	LiNGAM approach /
作者:	Shimizu, Shohei.
出版者:	Tokyo :Springer Japan : : 2022.,
面頁冊數:	ix, 94 p. :ill., digital ;24 cm.
內容註:	Introduction -- Basic LiNGAM model -- Estimation of the basic LiNGAM model -- Evaluation of statistical reliability and model assumptions -- LiNGAM with hidden common causes -- Other extensions.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics. -
電子資源:	https://doi.org/10.1007/978-4-431-55784-5
ISBN:	9784431557845

Statistical causal discovery = LiNGAM approach /
Shimizu, Shohei.

Statistical causal discoveryLiNGAM approach /[electronic resource] :by Shohei Shimizu. - Tokyo :Springer Japan :2022. - ix, 94 p. :ill., digital ;24 cm. - SpringerBriefs in statistics. JSS research series in statistics,2364-0065. - SpringerBriefs in statistics.JSS research series in statistics..

Introduction -- Basic LiNGAM model -- Estimation of the basic LiNGAM model -- Evaluation of statistical reliability and model assumptions -- LiNGAM with hidden common causes -- Other extensions.

This is the first book to provide a comprehensive introduction to a new semiparametric causal discovery approach known as LiNGAM, with the fundamental background needed to understand it. It offers a general overview of the basics of the LiNGAM approach for causal discovery, estimation principles, and algorithms. This semiparametric approach is one of the most exciting new topics in the field of causal discovery. The new framework assumes parametric assumptions on the functional forms of structural equations but makes no assumption on the distributions of exogenous variables other than non-Gaussianity. It provides data-analysis tools capable of estimating a much wider class of causal relations even in the presence of hidden common causes. This feature is in contrast to conventional nonparametric approaches based on conditional independence of variables. This book is highly recommended to readers who seek an in-depth and up-to-date overview of this new causal discovery approach to advance the technique as well as to those who are interested in applying this approach to real-world problems. This LiNGAM approach should become a standard item in the toolbox of statisticians, machine learners, and practitioners who need to perform observational studies.

ISBN: 9784431557845

Standard No.: 10.1007/978-4-431-55784-5doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276 / .S45 2022

Dewey Class. No.: 519.5

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