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Semiparametric Structural Equation Models for Causal Discovery, 1st ed. 2019 JSS Research Series in Statistics Series

Langue : Anglais

Auteur :

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.
1. Introduction.- 2. Basics of causal inference.- 3. Basic LiNGAM.- 4. Extensions.- 5. Applications.- 6. Conclusion.
Shohei Shimizu, Associate Professor, Osaka University

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

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