A one-of-a-kind resource on identifying and dealing with bias in
statistical research on causal effects
Do cell phones cause cancer? Can a new curriculum increase
student achievement? Determining what the real causes of such
problems are, and how powerful their effects may be, are central
issues in research across various fields of study. Some researchers
are highly skeptical of drawing causal conclusions except in
tightly controlled randomized experiments, while others discount
the threats posed by different sources of bias, even in less
rigorous observational studies. Bias and Causation presents a
complete treatment of the subject, organizing and clarifying the
diverse types of biases into a conceptual framework. The book
treats various sources of bias in comparative studies--both
randomized and observational--and offers guidance on how they
should be addressed by researchers.
Utilizing a relatively simple mathematical approach, the author
develops a theory of bias that outlines the essential nature of the
problem and identifies the various sources of bias that are
encountered in modern research. The book begins with an
introduction to the study of causal inference and the related
concepts and terminology. Next, an overview is provided of the
methodological issues at the core of the difficulties posed by
bias. Subsequent chapters explain the concepts of selection bias,
confounding, intermediate causal factors, and information bias
along with the distortion of a causal effect that can result when
the exposure and/or the outcome is measured with error. The book
concludes with a new classification of twenty general sources of
bias and practical advice on how mathematical modeling and expert
judgment can be combined to achieve the most credible causal
Throughout the book, examples from the fields of medicine,
public policy, and education are incorporated into the presentation
of various topics. In addition, six detailed case studies
illustrate concrete examples of the significance of biases in
Requiring only a basic understanding of statistics and
probability theory, Bias and Causation is an excellent supplement
for courses on research methods and applied statistics at the
upper-undergraduate and graduate level. It is also a valuable
reference for practicing researchers and methodologists in various
fields of study who work with statistical data.
This book was selected as the 2011 Ziegel Prize Winner in
Technometrics for the best book reviewed by the journal.
It is also the winner of the 2010 PROSE Award for Mathematics
from The American Publishers Awards for Professional and Scholarly
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