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.