Confounders (or confounding variables) pose significant challenges to detecting reliable causal relationships in observational studies. When data are collected from naturally occurring phenomena—e.g., mining software repositories (MSR)—researchers cannot rely on randomization to control for confounders, leading to biased causal inferences. Alternative approaches are required to mitigate confounding bias when exploring causal inferences. This paper explains and exemplifies the use of matching in MSR.