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Deconfounding confounding part 2: using directed acyclic graphs (DAGs)

John R Attia, Christopher Oldmeadow, Elizabeth G Holliday and Michael P Jones
Med J Aust 2017; 206 (11): . || doi: 10.5694/mja16.01167
Published online: 19 June 2017

In the previous article on confounding in this series,1 we presented the traditional explanation of a confounder. Over the past few decades, it has become clear that this definition has many limitations. For example, confounding can be induced by a network of variables rather than just a single variable, and adjusting for potential confounders can paradoxically increase confounding.


  • 1 Centre for Clinical Epidemiology and Biostatistics, University of Newcastle, Newcastle, NSW
  • 2 John Hunter Hospital, Newcastle, NSW
  • 3 Hunter Medical Research Institute, Newcastle, NSW
  • 4 Macquarie University, Sydney, NSW


Correspondence: john.attia@newcastle.edu.au

Competing interests:

No relevant disclosures.

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  • 3. Glymour MM, Greenland S. Causal diagrams. In: Rothman KJ, Greenland S, Lash TL, editors. Modern epidemiology, 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins, 2008; 183-209.
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  • 6. Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology 2009; 20: 488-495.
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  • 8. Greenland S, Pearce N. Statistical foundations for model-based adjustments. Annu Rev Public Health 2015; 36: 89-108.

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