David H. Rehkopf|M. Maria Glymour|Theresa L. Osypuk
The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose Is Not a Rose
Stanford University|Arista (United States)|University of California, San Francisco|University of Minnesota
assumption exposures measured observational settings clear specific definitions underpins epidemiologic research allows us use observational data predict outcomes interventions leap exposures measured exposures intervened upon typically supported consistency assumption consistency assumption received extensive attention risk factor epidemiology relatively little emphasis social epidemiology however violations consistency assumption may especially important consider understanding social economic exposures influence health efforts clarify definitions exposures thus bolstering consistency assumption help guide interventions improve population health reduce health disparities article focuses consistency assumption considered within social epidemiology explain assumption articulated causal inference literature give examples might violated three common exposures social epidemiology research income education neighborhood characteristics conclude good reason worry consistency assumption violations much social epidemiology research theoretically motivated explorations mechanisms along empirical comparisons research findings alternative operationalizations exposure help identify consistency violations recommend future social epidemiology studies explicit name discuss consistency assumption describing exposure interest including reconciling disparate results literature