Underdetermination and incommensurability in contemporary epidemiology

Kennedy Inst Ethics J. 1997 Jun;7(2):107-27. doi: 10.1353/ken.1997.0018.


In the shadowy world between philosophy of science and ethics lie the paired concepts of underdetermination and incommensurability. Typically, scientific evidence underdetermines the hypotheses tested in research studies, providing neither proof nor disproof. As a result, scientists must judge the weight of the evidence, and in doing so, bring scientific and extrascientific values to bear in their approaches to assessing and interpreting the evidence. When different scientists employ very different values, their views are said to be incommensurable. Less prominent differences represent partial incommensurabilities. The definitions and analyses provided by McMullin and by Veatch and Stempsey lay the foundation for the description of partial incommensurabilities in the current practice of assessing and interpreting epidemiologic evidence. This practice is called "causal inference" and is undertaken for the purpose of making causal conclusions and public health recommendations from population-based studies of exposures and diseases. Following the work of Bayley and Longino, several suggestions are examined for dealing with the partial incommensurabilities found in the general practice of causal inference in contemporary epidemiology. Two specific examples illustrate these ideas: studies on the relationship between induced abortion and breast cancer and those on the relationship between moderate alcohol consumption and breast cancer.

MeSH terms

  • Abortion, Induced
  • Alcoholism
  • Bioethics
  • Biomedical Research
  • Breast Neoplasms
  • Epidemiology*
  • Human Experimentation
  • Humans
  • Information Dissemination
  • Information Services
  • Interdisciplinary Communication
  • Interprofessional Relations
  • Mass Media
  • Medicine
  • Methods
  • Philosophy*
  • Public Health
  • Qualitative Research
  • Random Allocation
  • Reference Standards
  • Research
  • Research Design
  • Research Personnel*
  • Risk
  • Science*
  • Social Values*