[D.O.W.N.L.O.A.D] Semiparametric Structural Equation Models for Causal Discovery (SpringerBriefs in Statistics) R.A.R

[Ebook] Semiparametric Structural Equation Models for Causal Discovery (SpringerBriefs in Statistics) [R.A.R]

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F.R.E.E Semiparametric Structural Equation Models for Causal Discovery (SpringerBriefs in Statistics) P.P.T

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.