Statistical Causal Inferences and Their Applications in Public Health Research
Editors: Hua He, Pan Wu, Ding-Geng (Din) Chen
Includes software and data sets so readers may replicate analyses
Contains much needed coverage of recent developments in causal inference
Begins with an introduction to the concept of potential outcomes as applicable to causal inference concepts, models, and assumptions
This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in statistics, biostatistics, and computational biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
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