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Causal inference from randomized trials in social epidemiology

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  • Kaufman, Jay S.
  • Kaufman, Sol
  • Poole, Charles

Abstract

Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial confounding. Some optimism has been expressed about randomized social interventions as a solution to this long-standing inferential problem. We review the causal inference problem in social epidemiology, and the potential for causal inference in randomized social interventions. Using the example of a currently on-going intervention that randomly assigns families to non-poverty housing, we review the limitations to causal inference even under experimental conditions and explain which causal effects become identifiable. We note the benefit of using the randomized trial as a conceptual model, even for design and interpretation of observational studies in social epidemiology.

Suggested Citation

  • Kaufman, Jay S. & Kaufman, Sol & Poole, Charles, 2003. "Causal inference from randomized trials in social epidemiology," Social Science & Medicine, Elsevier, vol. 57(12), pages 2397-2409, December.
  • Handle: RePEc:eee:socmed:v:57:y:2003:i:12:p:2397-2409
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    Cited by:

    1. Galea, Sandro & Freudenberg, Nicholas & Vlahov, David, 2005. "Cities and population health," Social Science & Medicine, Elsevier, vol. 60(5), pages 1017-1033, March.
    2. Mooney, Stephen J. & El-Sayed, Abdulrahman M., 2016. "Stigma and the etiology of depression among the obese: An agent-based exploration," Social Science & Medicine, Elsevier, vol. 148(C), pages 1-7.
    3. Shai Mulinari & Sol Pia Juárez & Philippe Wagner & Juan Merlo, 2015. "Does Maternal Country of Birth Matter for Understanding Offspring’s Birthweight? A Multilevel Analysis of Individual Heterogeneity in Sweden," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-19, May.
    4. Riccardo Rebonato, 2016. "Mostly Harmless Econometrics: An Empiricist’s Companion; Mastering ‘Metrics: The Path from Cause to Effect," Quantitative Finance, Taylor & Francis Journals, vol. 16(7), pages 1009-1013, July.

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