Directed acyclic graphs (DAGs) are increasingly recommended or required by journals for observational studies making causal claims yet few paediatric observational studies present DAGs to support their analytical approach. This practical guide addresses the implementation gap between methodological guidance and research practice by clarifying when DAGs are needed (causal questions only, not descriptive or predictive research), demonstrating how to construct them for paediatric studies, and explaining how to use them to identify appropriate statistical adjustment strategies. We illustrate common pitfalls, including adjusting for colliders and mediators, address practical challenges such as temporal ordering ambiguity, and provide a worked example from recent paediatric literature. Proper use of DAGs makes causal assumptions explicit, prevents common analytical errors and strengthens causal inference in observational paediatric research.