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.

Autorentext

Shohei Shimizu, Associate Professor, Osaka University

Klappentext

This is the first book to provide a comprehensive introduction to a new modeling framework known as semiparametric structural equation modeling and its technique, with the fundamental background needed to understand it. It offers a general overview of the basics of semiparametric structural equation models for causal discovery, estimation principles and algorithms, and applications in neuroscience, economics, epidemiology, and more.

Semiparametric structural equation modeling is one of the most exciting new topics in the field of causal discovery. This 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 causal relations even in the presence of hidden common causes. This feature is in contrast to conventional nonparametric and parametric approaches. This book is highly recommended to readers who seek an in-depth and up-to-date overview about this new semiparametric approach to advance the new technique as well as to those who are interested in applying this new approach to real-world problems. This new semiparametric approach should become a standard item in the toolbox of statisticians, machine learners, and practitioners who need to perform observational studies.

Inhalt