In statistics, a confounder (also confounding variable/factorextraneous determinant or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. in other words, one or more effects cannot unambiguously be attributed to a single factor or interaction.

Illustration of a simple confounding factor. In other words, Z is the cause of X and Y.
Illustration of a simple confounding factor. In other words, Z is the cause of X and Y.

This is defined in terms of the data generating model (as in the figure to the right). Let X be some independent variable, and Y some dependent variable. To estimate the effect of X on Y, the statistician must suppress the effects of extraneous variables that influence both X and Y. We say that X and Y are confounded by some other variable Z whenever Z causally influences both X and Y.

In the case of risk assessments evaluating the magnitude and nature of risk to human health, it is important to control for confounding to isolate the effect of a particular hazard such as a food additive, pesticide, or new drug. For prospective studies, it is difficult to recruit and screen for volunteers with the same background (age, diet, education, geography, etc.), and in historical studies, there can be similar variability. Due to the inability to control for variability of volunteers and human studies, confounding is a particular challenge. For these reasons, experiments offer a way to avoid most forms of confounding.

In some disciplines, confounding is categorized into different types. In epidemiology, one type is “by indication”, which relates to observational studies. Because prognostic factors may influence treatment decisions (and bias estimates of treatment effects), controlling for known prognostic factors may reduce this problem, but it is always possible that a forgotten or unknown factor was not included or that factors interact complexly. Confounding by indication has been described as the most important limitation of observational studies. Randomized trials are not affected by this due to random assignment.

A confounder variable can also be categorized according to their source.

  • An operational confound can occur in both experimental and non-experimental research designs. This type occurs when a measure designed to assess a particular construct inadvertently measures something else as well.
  • procedural confound can occur in a laboratory experiment or a quasi-experiment. This type of confound occurs when the researcher mistakenly allows another variable to change along with the manipulated independent variable.
  • person confound occurs when two or more groups of units are analyzed together (e.g., workers from different occupations), despite varying according to one or more other (observed or unobserved) characteristics (e.g., gender).


Wikipedia. Confounding.