Causal inference challenges in social epidemiology: Bias, specificity, and imagination

M Maria Glymour; Kara E Rudolph

doi:10.1016/j.socscimed.2016.07.045

Causal inference challenges in social epidemiology: Bias, specificity, and imagination

Soc Sci Med. 2016 Oct:166:258-265. doi: 10.1016/j.socscimed.2016.07.045. Epub 2016 Aug 4.

Authors

M Maria Glymour¹, Kara E Rudolph²

Affiliations

¹ Department of Epidemiology and Biostatistics, University of California, San Francisco, USA. Electronic address: [email protected].
² Center for Health and Community, University of California, San Francisco, USA; School of Public Health, University of California, Berkeley, USA.

PMID: 27575286
DOI: 10.1016/j.socscimed.2016.07.045

No abstract available

Keywords: Causal inference; Fixed effects models; Instrumental variables; Population health; Propensity scores; Research methods; Social epidemiology.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Bias
Confounding Factors, Epidemiologic
Epidemiology / organization & administration*
Epidemiology / standards
Health Status Disparities
Humans
Models, Statistical
Propensity Score
Reproducibility of Results
Social Determinants of Health
Social Sciences / organization & administration*
Social Sciences / standards

Grants and funding

R01 MD006064/MD/NIMHD NIH HHS/United States