Konfounding

Konfounding is a term used in causal inference and epidemiology to describe the threat that unmeasured confounding variables pose to causal interpretation in observational studies. It refers to the situation in which an observed association between an exposure and an outcome could be entirely or largely explained by one or more unmeasured confounders that are related to both the exposure and the outcome. Konfounding emphasizes the possibility that hidden factors, rather than a direct causal effect, drive the association.

The concept encompasses the idea that confounding can distort estimated effects, potentially producing false positives (spurious associations) or masking true effects. It is distinct from random error but often interacts with other biases such as measurement error or selection bias. Konfounding is a motivation for robustness checks and sensitivity analyses that assess how strong an unmeasured confounder would need to be to alter the study’s conclusions.

Common methods to evaluate konfounding include sensitivity analyses, probabilistic bias analysis, and bounding approaches. The E-value, Rosenbaum bounds, and directed acyclic graphs (DAGs) are frequently used tools to quantify or visualize the potential impact of unmeasured confounding. These analyses do not prove the absence of konfounding but help researchers judge the credibility of causal claims and guide study design toward more robust conclusions, such as through better control of confounding, instrumental variables, or randomization.