Causal diagrams for encoding and evaluation of information bias

Eyal Shahar

Research output: Contribution to journalShort surveypeer-review

23 Scopus citations


Background Epidemiologists and clinical researchers usually classify bias into three main categories: confounding, selection bias and information bias. Previous authors have described the first two categories in the logic and notation of causal diagrams, formally known as directed acyclic graphs (DAG). Methods I examine common types of information bias - disease-related and exposure-related - from the perspective of causal diagrams. Results Disease or exposure information bias always involves the use of an effect of the variable of interest - specifically, an effect of true disease status or an effect of true exposure status. The bias typically arises from a causal or an associational path of no interest to the researchers. In certain situations, it may be possible to prevent or remove some of the bias. Conclusions Common types of information bias, just like confounding and selection bias, have a clear and helpful representation within the framework of causal diagrams.

Original languageEnglish (US)
Pages (from-to)436-440
Number of pages5
JournalJournal of Evaluation in Clinical Practice
Issue number3
StatePublished - Jun 2009


  • Causal diagram
  • Information bias
  • Surrogate variables

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health


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