Evaluating public health interventions: 5. Causal inference in public health research-do sex, race, and biological factors cause health outcomes?

M.M. Glymour and D. Spiegelman

American Journal of Public Health, 2017, vol. 107, issue 1, 81-85

Abstract: Counterfactual frameworks and statistical methods for supporting causal inference are powerful tools to clarify scientific questions and guide analyses in public health research. Counterfactual accounts of causation contrast what would happen to a population's health under alternative exposure scenarios. A long-standing debate in counterfactual theory relates to whether sex, race, and biological characteristics, including obesity, should be evaluated as causes, given that these variables do not directly correspond to clearly defined interventions. We argue that sex, race, and biological characteristics are important health determinants. Quantifying the overall health effects of these variables is often a natural starting point for disparities research. Subsequent assessments of biological or social pathways mediating those effects can facilitate the development of interventions designed to reduce disparities.

Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.2105/AJPH.2016.303539

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aph:ajpbhl:10.2105/ajph.2016.303539_4

DOI: 10.2105/AJPH.2016.303539

Access Statistics for this article

American Journal of Public Health is currently edited by Alfredo Morabia

More articles in American Journal of Public Health from American Public Health Association
Bibliographic data for series maintained by Christopher F Baum ().