Seeking causal explanations in social epidemiology

Social factors are associated with a wide variety of health outcomes. Socia l epidemiology has successfully used the traditional methods of surveillanc e and description to establish consistent relations between social factors and health status. Epidemiology as an etiologic science, however, has been largely ineffective in moving toward causal explanations for these observed patterns. Using the counterfactual approach to causal inference, the autho rs describe several fundamental problems that often arise when researchers seek to infer explanatory mechanisms from data on social factors. Contrasts that form standard causal effect estimates require implicit unobserved (co unterfactual) quantities, because observational data provide only one expos ure state for each individual. Although application of counterfactual argum ents has successfully advanced etiologic understanding in other observation al settings, the particular nature of social factors often leads to logical contradictions or misleading inferences when investigators fail to clearly articulate the counterfactual contrasts that are implied. For example, bec ause social factors are often attributes of individuals and are components of structured social relations, random assignment is not plausible even as a hypothetical experiment, making typical epidemiologic contrasts inappropr iate and the inference equivocal at best. Accordingly, more deliberate and creative approaches to causal inference in social epidemiology are required . Infectious disease epidemiology and systems analysis provide examples of approaches to causal inference that can be used when statistical mimicry of simple experimental designs is not tenable. In an era of increasing social inequality, valid approaches for the study of social factors and health ar e needed more urgently than ever.