\textit{Background:} Studies in software engineering are often particularly useful if they make causal claims because this allows practitioners to identify how they can influence outcomes of interest. Unfortunately, many non-experimental studies suffer from potential endogeneity through omitted confounding variables, which precludes claims of causality. \textit{Aims and Method:} We introduce instrumental variables and two-stage models as a means to account for endogeneity to the field of empirical software engineering. \textit{Results and Conclusions:} We define endogeneity, explain its primary cause, and lay out the idea behind instrumental variable approaches and two-stage models.