A one-of-a-kind resource on identifying and dealing with bias instatistical research on causal effects
Do cell phones cause cancer? Can a new curriculum increasestudent achievement? Determining what the real causes of suchproblems are, and how powerful their effects may be, are centralissues in research across various fields of study. Some researchersare highly skeptical of drawing causal conclusions except intightly controlled randomized experiments, while others discountthe threats posed by different sources of bias, even in lessrigorous observational studies. Bias and Causation presents acomplete treatment of the subject, organizing and clarifying thediverse types of biases into a conceptual framework. The booktreats various sources of bias in comparative studies--bothrandomized and observational--and offers guidance on how theyshould be addressed by researchers.
Utilizing a relatively simple mathematical approach, the authordevelops a theory of bias that outlines the essential nature of theproblem and identifies the various sources of bias that areencountered in modern research. The book begins with anintroduction to the study of causal inference and the relatedconcepts and terminology. Next, an overview is provided of themethodological issues at the core of the difficulties posed bybias. Subsequent chapters explain the concepts of selection bias,confounding, intermediate causal factors, and information biasalong with the distortion of a causal effect that can result whenthe exposure and/or the outcome is measured with error. The bookconcludes with a new classification of twenty general sources ofbias and practical advice on how mathematical modeling and expertjudgment can be combined to achieve the most credible causalconclusions.
Throughout the book, examples from the fields of medicine,public policy, and education are incorporated into the presentationof various topics. In addition, six detailed case studiesillustrate concrete examples of the significance of biases ineveryday research.
Requiring only a basic understanding of statistics andprobability theory, Bias and Causation is an excellent supplementfor courses on research methods and applied statistics at theupper-undergraduate and graduate level. It is also a valuablereference for practicing researchers and methodologists in variousfields of study who work with statistical data.
This book was selected as the 2011 Ziegel PrizeWinner in Technometrics for the best book reviewed by thejournal.
It is also the winner of the 2010 PROSE Award forMathematics from The American Publishers Awards forProfessional and Scholarly Excellence