One to many matching is employed to control for confounding variables that might otherwise distort the association between the exposure and the outcome. By matching each case to multiple controls, researchers can more accurately estimate the effect size and increase the precision of their findings. This method also helps to ensure that the matched controls are similar to the cases in terms of key confounding variables, such as age, sex, and socio-economic status.
