Measuring Racial Discrimination (2004) / Chapter Skim
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5 Causal Inference and the Assessment of Racial Discrimination
5 Causal Inference and the Assessment of Racial Discrimination
Pages 77-89
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From page 77... ...
Answering this question is fundamental to being able to conclude that there is a causal relationship between race and discrimination, which, in turn, is necessary to conclude that race-based discriminatory behaviors or processes contributed to an observed differential outcome. To illustrate the problem, we turn to a classic Dr.
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From page 78... ...
DRAWING CAUSAL INFERENCES In the context of measuring racial discrimination, researchers have developed alternative methods to answer the above counterfactual question and assess the incidence and effects of racial discrimination. A formal account of the counterfactual approach to causal inference provides a foundation for evaluating alternative solutions.
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From page 79... ...
The entire enterprise of causal inference is centered on alternative approaches for overcoming our inability to observe both of these outcomes for a single individual.3 Imagine we want to estimate the effect of discrimination on earnings as experienced by a black person. At the individual level, the unit causal effect of racial discrimination (here, discrimination against a black individual)
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From page 80... ...
This use of conditional independence allows the tie-in to the formal structure for causal inference we have just described. These diagrams offer a way to visualize causal relationships and the role of counterfactuals.
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From page 81... ...
knowledge of population averages of outcomes among aggregates of members of a racial group to estimate the average effect of racial discrimination. Study Design and Statistical Methods Research design is critical to the ability to draw causal inferences from data analysis.
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From page 82... ...
In any well-designed and well-executed experiment, randomization allows researchers to dismiss competing explanations as highly unlikely, but they are not entirely eliminated. For this reason, independent replication is important.
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From page 83... ...
Statistical methods developed for drawing causal inferences are organized largely around trying to re-create, from observational data, the circumstances that would have occurred had controlled experimental data been collected. These statistical methods are discussed in some detail in Chapter 7, where we critically review the use of statistical models, particularly regression models, to draw valid causal inferences from observational data.
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From page 84... ...
suggested that people's genetic makeup might predispose them both to smoking and to developing lung cancer. This alternative explanation for the association between smoking and lung cancer was dismissed only after studies of identical twins revealed that a smoking twin was more likely to develop lung cancer than a nonsmoking twin (see further discussion below)
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From page 85... ...
Researchers can learn how the accumulation of evidence from multiple sources with a variety of research designs contributes to causal inference by examining a widely cited example of inferring causation in nonexperimental settings -- the connection between smoking and lung cancer (see Box 5-2)
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From page 86... ...
Rosenbaum (2002:224) writes: "A nontrivial replication disrupts the circumstances of the original study, to check whether the treatment produced its ostensible effect, not some irrelevant circumstance." Nontrivial replication permits researchers to exclude alternative explanations for the phenomenon of interest, and therefore to distinguish between mere associa
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From page 87... ...
tions and actual causal relationships. It was the consistent pattern of evidence across studies with a variety of designs and conducted in a variety of contexts that permitted researchers to conclude that the association between smoking and lung cancer is causal.
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From page 88... ...
All research methods have particular strengths and weaknesses with respect to measuring racial discrimination, particularly concerning the extent to which they support causal inferences. In particular, experimental designs facilitate causal inference but limit generalization, whereas observational designs facilitate generalization while limiting causal inference.
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From page 89... ...
CAUSAL INFERENCE 89 Foundation, the National Institutes of Health, and private founda tions -- and the research community should embrace a multidisciplinary, multimethod approach to the measurement of racial discrimination and seek improvements in all major methods employed.
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