 The (mis)estimation of neighborhood effects: causal inference for a practicable social epidemiologyJ. Michael Oakes Social Science & Medicine, 2004, vol. 58, issue 10, 1929-1952 Abstract: 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. Keywords: HLM; Mixed; model; Cluster; trial; Community; trial; Counterfactual; Assignment; mechanism; Propensity; score (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:socmed:v:58:y:2004:i:10:p:1929-1952 Ordering information: This journal article can be ordered from Access Statistics for this article Social Science & Medicine is currently edited by Ichiro (I.) Kawachi and S.V. (S.V.) Subramanian More articles in Social Science & Medicine from Elsevier |
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