Bias And Causation: Models And Judgment For Valid Comparisons

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.

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.

"A consultant who specializes in applying statistics to various business and legal issues, Weisberg explains approaches to bias and causal inference, a realm statisticians have avoided until recently because it requires intuitive skills beyond the pale of mathematics. He writes for practicing researchers and methodologists and for students with a reasonably solid grounding in basic statistics and research methods." (SciTech Book News, December 2010)

Preface.
1. What Is Bias?
1.1 Apples and Oranges.
1.2 Statistics vs. Causation.
1.3 Bias in the Real World.
Guidepost 1.
2. Causality and Comparative Studies.
2.1 Bias and Causation.
2.2 Causality and Counterfactuals.
2.3 Why Counterfactuals?
2.4 Causal Effects.
2.5 Empirical Effects.
Guidepost 2.
3. Estimating Causal Effects.
3.1 External Validity.
3.2 Measures of Empirical Effects.
3.3 Difference of Means.
3.4 Risk Difference and Risk Ratio.
3.5 Potential Outcomes.
3.6 Time-Dependent Outcomes.
3.7 Intermediate Variables.
3.8 Measurement of Exposure.
3.9 Measurement of the Outcome Value.
3.10 Confounding Bias.
Guidepost 3.
4. Varieties of Bias.
4.1 Research Designs and Bias.
4.2 Bias in Biomedical Research.
4.3 Bias in Social Science Research.
4.4 Sources of Bias: A Proposed Taxonomy.
Guidepost 4.
5. Selection Bias.
5.1 Selection Processes and Bias.
5.2 Traditional Selection Model: Dichotomous Outcome.
5.3 Causal Selection Model: Dichotomous Outcome.
5.4 Randomized Experiments.
5.5 Observational Cohort Studies.
5.6 Traditional Selection Model: Numerical Outcome.
5.7 Causal Selection Model: Numerical Outcome.
Guidepost 5.
Appendix.
6. Confounding: An Enigma?
6.1 What is the Real Problem?
6.2 Confounding and Extraneous Causes.
6.3 Confounding and Statistical Control.
6.4 Confounding and Comparability.
6.5 Confounding and the Assignment Mechanism.
6.6 Confounding and Model Specifi cation.
Guidepost 6.
7. Confounding: Essence, Correction, and Detection.
7.1 Essence: The Nature of Confounding.
7.2 Correction: Statistical Control for Confounding.
7.3 Detection: Adequacy of Statistical Adjustment.
Guidepost 7.
Appendix.
8. Intermediate Causal Factors.
8.1 Direct and Indirect Effects.
8.2 Principal Stratifi cation.
8.3 Noncompliance.
8.4 Attrition.
Guidepost 8.
9. Information Bias.
9.1 Basic Concepts.
9.2 Classical Measurement Model: Dichotomous Outcome.
9.3 Causal Measurement Model: Dichotomous Outcome.
9.4 Classical Measurement Model: Numerical Outcome.
9.5 Causal Measurement Model: Numerical Outcome.
9.6 Covariates Measured with Error.
Guidepost 9.
10. Sources of Bias.
10.1 Sampling.
10.2 Assignment.
10.3 Adherence.
10.4 Exposure Ascertainment.
10.5 Outcome Measurement.
Guidepost 10.
11. Contending with Bias.
11.1 Conventional Solutions.
11.2 Standard Statistical Paradigm.
11.3 Toward a Broader Perspective.
11.4 Real-World Bias Revisited.
11.5 Statistics and Causation.
Glossary.
Bibliography.
Index.