Statistical Causal Discovery: LiNGAM Approach
Seiten
2022
|
1st ed. 2022
Springer Verlag, Japan
978-4-431-55783-8 (ISBN)
Springer Verlag, Japan
978-4-431-55783-8 (ISBN)
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.
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
Introduction.- Basic LiNGAM model.- Estimation of the basic LiNGAM model.- Evaluation of statistical reliability and model assumptions.- LiNGAM with hidden common causes.- Other extensions.
| Erscheint lt. Verlag | 10.10.2022 |
|---|---|
| Reihe/Serie | JSS Research Series in Statistics |
| JSS Research Series in Statistics | |
| SpringerBriefs in Statistics | SpringerBriefs in Statistics |
| Zusatzinfo | 19 Illustrations, black and white; IX, 94 p. 19 illus. |
| Verlagsort | Tokyo |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra | |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | Causal Discovery • causal inference • lingam • Observational data • Structural Equation Modeling |
| ISBN-10 | 4-431-55783-0 / 4431557830 |
| ISBN-13 | 978-4-431-55783-8 / 9784431557838 |
| Zustand | Neuware |
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