Confounding is a major concern in observational studies since it can distort the apparent association between exposure and outcome. One-to-one matching helps to control for confounding variables by ensuring that each case is matched with a control that shares similar attributes. This technique allows researchers to isolate the effect of the exposure of interest more effectively.
