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 tobe an important
reference.
