A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effectsDo 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 is the winner of the 2010 PROSE Award for Mathematics from The American Publishers Awards for Professional and Scholarly Excellence
Author(s): Herbert I. Weisberg
Edition: 1
Year: 2010
Language: English
Pages: 348
Bias and Causation: Models and Judgment for Valid Comparisons......Page 2
Contents......Page 10
Preface......Page 14
CHAPTER 1: What Is Bias?......Page 20
1.1 APPLES AND ORANGES......Page 21
1.2 STATISTICS VS. CAUSATION......Page 22
1.3 BIAS IN THE REAL WORLD......Page 25
GUIDEPOST 1......Page 42
2.1 BIAS AND CAUSATION......Page 43
2.2 CAUSALITY AND COUNTERFACTUALS......Page 45
2.3 WHY COUNTERFACTUALS?......Page 51
2.4 CAUSAL EFFECTS......Page 52
2.5 EMPIRICAL EFFECTS......Page 57
GUIDEPOST 2......Page 65
CHAPTER 3: Estimating Causal Effects......Page 66
3.1 EXTERNAL VALIDITY......Page 67
3.2 MEASURES OF EMPIRICAL EFFECTS......Page 69
3.3 DIFFERENCE OF MEANS......Page 71
3.4 RISK DIFFERENCE AND RISK RATIO......Page 74
3.5 POTENTIAL OUTCOMES......Page 76
3.6 TIME-DEPENDENT OUTCOMES......Page 79
3.7 INTERMEDIATE VARIABLES......Page 82
3.8 MEASUREMENT OF EXPOSURE......Page 83
3.9 MEASUREMENT OF THE OUTCOME VALUE......Page 87
3.10 CONFOUNDING BIAS......Page 89
GUIDEPOST 3......Page 90
CHAPTER: 4 Varieties of Bias......Page 91
4.1 RESEARCH DESIGNS AND BIAS......Page 92
4.2 BIAS IN BIOMEDICAL RESEARCH......Page 100
4.3 BIAS IN SOCIAL SCIENCE RESEARCH......Page 104
4.4 SOURCES OF BIAS: A PROPOSED TAXONOMY......Page 109
GUIDEPOST 4......Page 111
5.1 SELECTION PROCESSES AND BIAS......Page 112
5.2 TRADITIONAL SELECTION MODEL: DICHOTOMOUS OUTCOME......Page 119
5.3 CAUSAL SELECTION MODEL: DICHOTOMOUS OUTCOME......Page 121
5.4 RANDOMIZED EXPERIMENTS......Page 123
5.5 OBSERVATIONAL COHORT STUDIES......Page 127
5.6 TRADITIONAL SELECTION MODEL: NUMERICAL OUTCOME......Page 130
5.7 CAUSAL SELECTION MODEL: NUMERICAL OUTCOME......Page 133
GUIDEPOST 5......Page 140
APPENDIX......Page 141
CHAPTER 6: Confounding: An Enigma?......Page 145
6.2 CONFOUNDING AND EXTRANEOUS CAUSES......Page 146
6.3 CONFOUNDING AND STATISTICAL CONTROL......Page 150
6.4 CONFOUNDING AND COMPARABILITY......Page 156
6.5 CONFOUNDING AND THE ASSIGNMENT MECHANISM......Page 158
6.6 CONFOUNDING AND MODEL SPECIFICATION......Page 160
GUIDEPOST 6......Page 163
CHAPTER 7: Confounding: Essence, Correction, and Detection......Page 164
7.1 ESSENCE: THE NATURE OF CONFOUNDING......Page 165
7.2 CORRECTION: STATISTICAL CONTROL FOR CONFOUNDING......Page 191
7.3 DETECTION: ADEQUACY OF STATISTICAL ADJUSTMENT......Page 199
GUIDEPOST 7......Page 210
APPENDIX......Page 211
8.1 DIRECT AND INDIRECT EFFECTS......Page 214
8.2 PRINCIPAL STRATIFICATION......Page 219
8.3 NONCOMPLIANCE......Page 228
8.4 ATTRITION......Page 233
GUIDEPOST 8......Page 234
CHAPTER 9: Information Bias......Page 236
9.1 BASIC CONCEPTS......Page 237
9.2 CLASSICAL MEASUREMENT MODEL: DICHOTOMOUS OUTCOME......Page 242
9.3 CAUSAL MEASUREMENT MODEL: DICHOTOMOUS OUTCOME......Page 249
9.4 CLASSICAL MEASUREMENT MODEL: NUMERICAL OUTCOME......Page 258
9.5 CAUSAL MEASUREMENT MODEL: NUMERICAL OUTCOME......Page 261
9.6 COVARIATES MEASURED WITH ERROR......Page 265
GUIDEPOST 9......Page 269
CHAPTER 10: Sources of Bias......Page 271
10.1 SAMPLING......Page 273
10.2 ASSIGNMENT......Page 279
10.3 ADHERENCE......Page 285
10.4 EXPOSURE ASCERTAINMENT......Page 288
10.5 OUTCOME MEASUREMENT......Page 292
GUIDEPOST 10......Page 296
CHAPTER 11: Contending with Bias......Page 298
11.1 CONVENTIONAL SOLUTIONS......Page 299
11.2 STANDARD STATISTICAL PARADIGM......Page 305
11.3 TOWARD A BROADER PERSPECTIVE......Page 307
11.4 REAL-WORLD BIAS REVISITED......Page 312
11.5 STATISTICS AND CAUSATION......Page 322
Glossary......Page 328
Bibliography......Page 340
Index......Page 359
