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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.
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.
Shohei Shimizu,
Professor, Shiga University
Team Leader, RIKEN
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
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Importe, Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
ix
94 S. 19 s/w Illustr. 94 p. 19 illus. |
| ISBN-13: | 9784431557838 |
| ISBN-10: | 4431557830 |
| Sprache: | Englisch |
| Herstellernummer: | 978-4-431-55783-8 |
| Einband: | Kartoniert / Broschiert |
| Autor: | Shimizu, Shohei |
| Auflage: | 1st edition 2022 |
| Hersteller: |
Springer Japan
Springer Japan KK |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 7 mm |
| Von/Mit: | Shohei Shimizu |
| Erscheinungsdatum: | 05.09.2022 |
| Gewicht: | 0,178 kg |
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