Statistical Causal Inferences and Their Applications in Public Health Research

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. 

Hua He, Ph.D. is an Assistant Professor in the Department of Biostatistics and Computational Biology and a member of the Division of Psychiatric Statistics (DPS), which focuses on methodological and collaborative research with investigators in the areas of behavioral and social sciences both within and outside of the UR Medical Center. She has been working in statistical and psychosocial research and has more than 40 publications in peer-reviewed journals and one book chapter on reliability statistics. She also co-authored a graduate-level textbook on “Applied Categorical and Count Data Analysis” by Chapman and Hall//CRC. Dr. He is highly experienced in longitudinal data analysis, structural equation models, potential outcome based causal models, distribution-free models, ROC analysis, functional response models, non-parametric analysis, survey data analysis and their applications to observational studies and randomized controlled trials across a range of disciplines, especially in the behavioral and social sciences. She is a member of the American Statistical Association (ASA) and Easter North American Region (ENAR) of the International Biometric Society. She also serves as a referee for Journal of Biometrics, Statistics in Medicine, Substance Abuse Treatment, Prevention and Policy, and Psychology and Aging etc.