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Bias and Causation: Models and Judgment for Valid Comparisons Book

Bias and Causation: Models and Judgment for Valid Comparisons
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  • Bias and Causation: Models and Judgment for Valid Comparisons
  • Written by author Herbert I. Weisberg
  • Published by Wiley, John & Sons, Incorporated, 1/6/2011
  • 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 a
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Preface xi

1 What Is Bias? 1

1.1 Apples and Oranges 2

1.2 Statistics vs. Causation 3

1.3 Bias in the Real World 6

Guidepost 1 23

2 Causality and Comparative Studies 24

2.1 Bias and Causation 24

2.2 Causality and Counterfactuals 26

2.3 Why Counterfactuals? 32

2.4 Causal Effects 33

2.5 Empirical Effects 38

Guidepost 2 46

3 Estimating Causal Effects 47

3.1 External Validity 48

3.2 Measures of Empirical Effects 50

3.3 Difference of Means 52

3.4 Risk Difference and Risk Ratio 55

3.5 Potential Outcomes 57

3.6 Time-Dependent Outcomes 60

3.7 Intermediate Variables 63

3.8 Measurement of Exposure 64

3.9 Measurement of the Outcome Value 68

3.10 Confounding Bias 70

Guidepost 3 71

4 Varieties of Bias 72

4.1 Research Designs and Bias 73

4.2 Bias in Biomedical Research 81

4.3 Bias in Social Science Research 85

4.4 Sources of Bias: A Proposed Taxonomy 90

Guidepost 4 92

5 Selection Bias 93

5.1 Selection Processes and Bias 93

5.2 Traditional Selection Model: Dichotomous Outcome 100

5.3 Causal Selection Model: Dichotomous Outcome 102

5.4 Randomized Experiments 104

5.5 Observational Cohort Studies 108

5.6 Traditional Selection Model: Numerical Outcome 111

5.7 Causal Selection Model: Numerical Outcome 114

Guidepost 5 121

Appendix 122

6 Confounding: An Enigma? 126

6.1 What is the Real Problem? 127

6.2 Confounding and Extraneous Causes 128

6.3 Confounding and Statistical Control 131

6.4 Confounding and Comparability 137

6.5 Confounding and the Assignment Mechanism 139

6.6 Confounding and Model Specification 141

Guidepost 6 144

7 Confounding: Essence, Correction, and Detection 145

7.1 Essence: The Nature of Confounding 146

7.2 Correction: Statistical Control for Confounding 172

7.3 Detection: Adequacy of Statistical Adjustment 180

Guidepost 7 191

Appendix 192

8 Intermediate Causal Factors 195

8.1 Direct and Indirect Effects 195

8.2 Principal Stratification 200

8.3 Noncompliance 209

8.4 Attrition 214

Guidepost 8 216

9 Information Bias 217

9.1 Basic Concepts 218

9.2 Classical Measurement Model: Dichotomous Outcome 223

9.3 Causal Measurement Model: Dichotomous Outcome 230

9.4 Classical Measurement Model: Numerical Outcome 239

9.5 Causal Measurement Model: Numerical Outcome 242

9.6 Covariates Measured with Error 246

Guidepost 9 250

10 Sources of Bias 252

10.1 Sampling 254

10.2 Assignment 260

10.3 Adherence 266

10.4 Exposure Ascertainment 269

10.5 Outcome Measurement 273

Guidepost 10 277

11 Contending with Bias 279

11.1 Conventional Solutions 280

11.2 Standard Statistical Paradigm 286

11.3 Toward a Broader Perspective 288

11.4 Real-World Bias Revisited 293

11.5 Statistics and Causation 303

Glossary 309

Bibliography 321

Index 340


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