The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiology.
The resurgence of interest in the effect of neighborhood contexts on health outcomes, motivated by advances in social epidemiology, multilevel theories and sophisticated statistical models, too often fails to confront the enormous methodological problems associated with causal inference. This paper employs the counterfactual causal framework to illuminate fundamental obstacles in the identification, explanation, and usefulness of multilevel neighborhood effect studies. We show that identifying useful independent neighborhood effect parameters, as currently conceptualized with observational data, to be impossible. Along with the development of a dependency-based methodology and theories of social interaction, randomized community trials are advocated as a superior research strategy, one that may help social epidemiology answer the causal questions necessary for remediating disparities and otherwise improving the public's health.
Oakes
J Michael
JM
Division of Epidemiology and Population Research Center, University of Minnesota, 1300 South 2nd Street, Minneapolis, MN 55454, USA. oakes@epi.umn.edu
D015982:Bias; D015984:Causality; D004812:Epidemiologic Methods; D006304:Health Status; D006801:Humans; D019370:Observation; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D012111:Residence Characteristics; D012931:Social Environment; D012962:Sociology, Medical / Q000379:methods*