Invited Commentary: Causal diagrams and measurement bias

Am J Epidemiol. 2009 Oct 15;170(8):959-62; discussion 963-4. doi: 10.1093/aje/kwp293. Epub 2009 Sep 15.


Causal inferences about the effect of an exposure on an outcome may be biased by errors in the measurement of either the exposure or the outcome. Measurement errors of exposure and outcome can be classified into 4 types: independent nondifferential, dependent nondifferential, independent differential, and dependent differential. Here the authors describe how causal diagrams can be used to represent these 4 types of measurement bias and discuss some problems that arise when using measured exposure variables (e.g., body mass index) to make inferences about the causal effects of unmeasured constructs (e.g., "adiposity"). The authors conclude that causal diagrams need to be used to represent biases arising not only from confounding and selection but also from measurement.

Publication types

  • Research Support, N.I.H., Extramural
  • Comment

MeSH terms

  • Bias*
  • Body Mass Index*
  • Causality*
  • Confounding Factors, Epidemiologic
  • Epidemiologic Methods*
  • Humans