Causal inference based on observational data often hinges on the assumption that all relevant confounding variables are captured in the model and only exogenous variation of the treatment variable is used for identification. However, when multiple potential controls are involved, researchers may overcontrol or miss crucial opportunities for rigorous causal modeling. In such cases, Directed Acyclic Graphs (DAGs) can help to find potential identification strategies and clarify underlying implicit assumptions.