Overview
- Presents semiparametric or non-Gaussian methods for causal discovery
- Explains methods that are capable of estimating causal direction in the presence of hidden common causes
- Provides an overview of applications of those semiparametric causal discovery methods
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Part of the book sub series: JSS Research Series in Statistics (JSSRES)
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Table of contents (7 chapters)
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Front Matter
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Basics of LiNGAM Approach
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Front Matter
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Extended Models
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Front Matter
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Keywords
About this book
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.
Authors and Affiliations
About the author
Shohei Shimizu,
Professor, Shiga University
Team Leader, RIKEN
Bibliographic Information
Book Title: Statistical Causal Discovery: LiNGAM Approach
Authors: Shohei Shimizu
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-4-431-55784-5
Publisher: Springer Tokyo
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive licence to Springer Japan KK 2022
Softcover ISBN: 978-4-431-55783-8Published: 05 September 2022
eBook ISBN: 978-4-431-55784-5Published: 04 September 2022
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: IX, 94
Number of Illustrations: 19 b/w illustrations
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences