*"Overall, this textbook is a perfect guide for interested researchers
and students who wish to understand the rationale and methods of causal
inference. Each chapter provides an R implementation of the introduced
causal concepts and models and concludes with appropriate exercises."
*-An-Shun Tai & Sheng-Hsuan Lin, in Biometrics
One of the primary motivations for clinical trials and observational
studies of humans is to infer cause and effect. Disentangling causation
from confounding is of utmost importance. Fundamentals of Causal
Inference explains and relates different methods of confounding
adjustment in terms of potential outcomes and graphical models,
including standardization, difference-in-differences estimation, the
front-door method, instrumental variables estimation, and propensity
score methods. It also covers effect-measure modification, precision
variables, mediation analyses, and time-dependent confounding. Several
real data examples, simulation studies, and analyses using R motivate
the methods throughout. The book assumes familiarity with basic
statistics and probability, regression, and R and is suitable for
seniors or graduate students in statistics, biostatistics, and data
science as well as PhD students in a wide variety of other disciplines,
including epidemiology, pharmacy, the health sciences, education, and
the social, economic, and behavioral sciences.
Beginning with a brief history and a review of essential elements of
probability and statistics, a unique feature of the book is its focus on
real and simulated datasets with all binary variables to reduce complex
methods down to their fundamentals. Calculus is not required, but a
willingness to tackle mathematical notation, difficult concepts, and
intricate logical arguments is essential. While many real data examples
are included, the book also features the Double What-If Study, based on
simulated data with known causal mechanisms, in the belief that the
methods are best understood in circumstances where they are known to
either succeed or fail. Datasets, R code, and solutions to odd-numbered
exercises are available on the book's website at
www.routledge.com/9780367705053. Instructors can also find slides based
on the book, and a full solutions manual under 'Instructor Resources'.
