Bias and Causation: Models and Judgment for Valid Comparisons

The cursory treatment of bias as a topic of serious consideration has resulted in a lack of easily accessible reference material. Bias and Causation organizes and clarifies the diverse and somewhat overlapping types of biases within a coherent framework. It provides a comprehensive discussion of the sources of bias in comparative studies (both randomized and observational) and how to address them, emphasizes systematic errors (i.e. bias) that affect proper interpretation of results, and draws concrete examples from biomedical and social science literature to illustrate and explain how biases arise in everyday practice. This will be a single go–to reference for scientific and legal researchers.

Spis treści:
Chapter 1: What is Bias?
1.1 Apples and Oranges.
1.2 Statistics vs. Causation.
1.3 Bias in the Real World.
Chapter 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.
Chapter 3: Empirical Effects and Bias.
3.1 External Validity.
3.2 Measures of Empirical Effects.
3.3 The Difference of Means.
3.4 The Risk Difference and Risk Ratio.
3.5 Potential Outcomes.
3.6 Time–Dependent Outcomes.
3.7 Intermediate Variables.
3.8 Measurement of Exposure Status.
3.9 Measurement of the Outcome Value.
3.10 Confounding Bias.
Chapter 4: Varieties of Bias.
4.1 Research Designs.
4.2 Bias in Biomedical Research.
4.3 Bias in Social Science Research.
4.4 Sources of Bias.
Chapter 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.
Appendix to Chapter 5.
Chapter 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.
Chapter 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.
Appendix to Chapter 7.
Chapter 8: Intermediate Causal Factors.
8.1 Direct and Indirect Effects.
8.2 Principal Stratification.
8.3 Noncompliance.
8.4 Attrition.
Chapter 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.
Chapter 10: Sources of Bias.
10.1 Sampling.
10.2 Assignment.
10.3 Adherence.
10.4 Exposure Ascertainment.
10.5 Outcome Measurement.
Chapter 11: Contending with Bias.
11.1 Conventional Solutions.
11.2 The Standard Statistical Paradigm.
11.3 Toward a Broader Perspective.
11.4 Real–World Bias Revisited.
11.5 Statistics and Causation.