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In this chapter we will focus on the scientific issues involved in establishing these sorts of causal claims. We will review the issues facing scientists or others who review evidence to collectively decide on population causal claims. The next chapter provides a framework for doing so. At the start of this chapter, we discuss the types of scientific information considered in evaluating the strength of evidence for inferring causation. Then we discuss how epidemiologists define and assess association and how association differs from causation. This distinction is essential to understanding prior approaches to presumptive disability decision making and also this Committee’s proposed approach. In Appendix J, we offer an extended discussion of what we mean by causation and how it is modeled statistically. We have placed this material in an appendix, not as a reflection of its importance, but because the topic is too complicated to cover in a short section.

Next we discuss the scientific strategies used to establish association, and lastly we discuss the scientific strategies used to move beyond just determining the presence of an association to inferring causation. We conclude the chapter by discussing uncertainty—both with respect to association and with respect to causation. We leave to the next chapter a discussion of strategies for synthesizing potentially diverse sources of evidence into a single overall judgment of the strength of evidence for a causal claim.


Evidence about population causal claims (hereafter just “causal claims”) comes from a variety of sources. In some cases, we have extensive knowledge about the mechanism by which exposure causes disease. For example, we do not need a randomized clinical trial to establish that bullet or shrapnel wounds have a deleterious effect on health. In other cases, such as low levels of exposure to lead and cognitive deficits in children, we know much less about the mechanisms and turn to other types of scientific evidence including findings of epidemiologic studies. Any scientific assessment of a causal claim combines the mechanistic knowledge and statistical evidence from epidemiologic studies. In this section we briefly survey the types of statistical evidence used to establish causal claims, and then we sketch the types of toxicologic, biologic, and mechanistic knowledge used to support or reject causal claims. By statistical evidence we mean the quantitative relationships between a set of measured variables in a sample. Case reports about individual patients may be useful for suggesting etiologic hypotheses, particularly with exposures that are followed quickly by disease onset.

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