| 书目名称 | Statistical Causal Discovery: LiNGAM Approach |
| 编辑 | Shohei Shimizu |
| 视频video | |
| 概述 | 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 ap |
| 丛书名称 | SpringerBriefs in Statistics |
| 图书封面 |  |
| 描述 | .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, mach |
| 出版日期 | Book 2022 |
| 关键词 | Causal Discovery; Causal Inference; LiNGAM; Observational Data; Structural Equation Modeling |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-4-431-55784-5 |
| isbn_softcover | 978-4-431-55783-8 |
| isbn_ebook | 978-4-431-55784-5Series ISSN 2191-544X Series E-ISSN 2191-5458 |
| issn_series | 2191-544X |
| copyright | The Author(s), under exclusive licence to Springer Japan KK 2022 |
发表于 2025-3-21 19:06:14
