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.

Mining Software Repositories studies have become increasingly popular over the years. However, a notable limitation is that they report correlational relationships rather than establishing causation. In contrast, certain disciplines (e.g. epidemiology) have developed specific methods to address this limitation. The goal of this tutorial is to introduce participants to one such method: cohort studies. By the end of the tutorial, participants will be familiar with the steps and techniques involved in designing and analyzing cohort studies.