Book description
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 conclusions.
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 everyday research.
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 Excellence
HERBERT I. WEISBERG
, PhD, is founder and President of Correlation Research Inc., a
consulting firm that specializes in the application of statistics to
various business and legal issues. Dr. Weisberg has over forty years of
statistical consulting experience and has published numerous articles
related to bias assessment and reduction.
