Since confounding obscures the real effect of the exposure, it is important to adequately address confounding for making valid causal inferences from observational data. Directed acyclic graphs (DAGs) are visual representations of causal assumptions that are increasingly used in modern epidemiology. They can help to identify the presence of confounding for the causal question at hand. This structured approach serves as a visual aid in the scientific discussion by making underlying relations explicit. This article explains the basic concepts of DAGs and provides examples in the field of nephrology with and without presence of confounding. Ultimately, these examples will show that DAGs can be preferable to the traditional methods to identify sources of confounding, especially in complex research questions.
Keywords: DAGs; causal; confounding; directed acyclic graph; epidemiology.
© The Author 2014. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.