Causal Inference in Perinatal Epidemiology: Revisiting the Birth Weight Paradox

Enrique F. Schisterman, Robert W. Platt

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Does smoking cause neonatal mortality? Understanding causation is essential to inform the creation of effective interventions aimed at improving human health. Causal diagrams in the form of directed acyclic graphs (DAGs) have changed the way we approach causal inference. This chapter provides an overview of causal thinking and the use of DAGs in helping to design etiologically-oriented epidemiologic research. It introduces the theory of DAGs, and adjustment for variables when estimating total effects, and when estimating direct and indirect effects is discussed. The "birth weight paradox" is used to illustrate the relevancy of causal analysis to reproductive and perinatal epidemiology. DAGs are used to help estimate different effects, to explain why z-scores remove the crossing of curves, and to help define overadjustment. DAGs provide a tool to aid in formulation of a research question and its accompanying analytic plan, and hence help ensure that the intended question is answered and the science interpreted within a formalized causal paradigm.

Original languageEnglish (US)
Title of host publicationReproductive and Perinatal Epidemiology
PublisherOxford University Press
ISBN (Print)9780199895328, 9780195387902
DOIs
StatePublished - May 1 2011
Externally publishedYes

Keywords

  • Birth weight paradox
  • Causal inference
  • Collider stratification bias
  • Counterfactuals
  • Direct effects
  • Directed acyclic graphs
  • Indirect effects
  • Overadjustment
  • Selection bias
  • Total effects
  • Z-scores

ASJC Scopus subject areas

  • Medicine(all)

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