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Preface
Author(s) -
Xin M. Tu
Publication year - 1992
Publication title -
clinical and experimental allergy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.462
H-Index - 154
eISSN - 1365-2222
pISSN - 0954-7894
DOI - 10.1111/j.1365-2222.1992.tb01765.x
Subject(s) - citation , library science , computer science , information retrieval
This book originated from a series of discussions among the editors when we were all at the University of Rochester, NY, before 2015. At that time, we had a research discussion group under the leadership of Professor Xin M. Tu that met biweekly to discuss the methodological development on statistical causal inferences and their applications to public health data. In this group, we got a closer overview of the principles and methods behind the statistical causal inferences which are needed to be disseminated to aid the further development in the area of public health research. We were convinced that this can be accomplished better through the compilation of a book in this area. This book compiles and presents new developments in statistical causal inference. Data and computer programs will be publicly available in order for readers to replicate model development and data analysis presented in each chapter so that these new methods can be readily applied by interested readers in their research. The book strives to bring together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development as well as applications. The book is timely and has high potential to impact model development and data analyses of causal inference across a wide spectrum of analysts, as well as fostering more research in this direction. The book consists of four parts which are presented in 15 chapters. Part I includes Chap. 1 with an overview on statistical causal inferences. This chapter introduces the concept of potential outcomes and its application to causal inference as well as the basic concepts, models, and assumptions in causal inference. Part II discusses propensity score method for causal inference which includes six chapters from Chaps. 2 to 7. Chapter 2 gives an overview of propensity score methods with underlying assumptions for using propensity score, and Chap. 3 addresses causal inference within Dawid’s decision-theoretic framework, where studies of “sufficient covariates” and their properties are essential. In addition, this chapter investigates the augmented inverse probability weighted (AIPW) estimator, which is a combination of a response model and a propensity model. It is found that, in the linear regression with homoscedasticity, propensity variable analysis provides exactly the same estimated causal effect as that from multivariate linear regression,

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