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The Resource Bias and causation : models and judgment for valid comparisons, Herbert I. Weisberg

Bias and causation : models and judgment for valid comparisons, Herbert I. Weisberg

Work
Publication
Extent
xv, 348 p.
Contents
  • 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 Specification
  • 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 Stratification
  • 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
Isbn
9780470286395
Instance
Label
Bias and causation : models and judgment for valid comparisons
Title
Bias and causation
Title remainder
models and judgment for valid comparisons
Statement of responsibility
Herbert I. Weisberg
Creator
Subject
Language
eng
Member of
Cataloging source
DLC
http://library.link/vocab/creatorDate
1944-
http://library.link/vocab/creatorName
Weisberg, Herbert I.
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
bibliography
Series statement
Wiley series in probability and statistics
http://library.link/vocab/subjectName
  • Discriminant analysis
  • Paired comparisons (Statistics)
Label
Bias and causation : models and judgment for valid comparisons, Herbert I. Weisberg
Instantiates
Publication
Bibliography note
Includes bibliographical references and index
Contents
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 Specification -- 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 Stratification -- 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
Control code
ocn500185781
Dimensions
25 cm
Extent
xv, 348 p.
Isbn
9780470286395
Isbn Type
(cloth)
Lccn
2009054242
Other physical details
ill.
System control number
(OCoLC)500185781
Label
Bias and causation : models and judgment for valid comparisons, Herbert I. Weisberg
Publication
Bibliography note
Includes bibliographical references and index
Contents
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 Specification -- 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 Stratification -- 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
Control code
ocn500185781
Dimensions
25 cm
Extent
xv, 348 p.
Isbn
9780470286395
Isbn Type
(cloth)
Lccn
2009054242
Other physical details
ill.
System control number
(OCoLC)500185781

Subject

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