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Abstract Recent developments in computer science have substantially advanced the use of observational causal inference under Pearl's structural causal model (SCM) framework. A key tool in the application of SCM is the use of casual diagrams, used to visualize the causal structure of a system or process under study. Here, we show how causal diagrams can be extended to ensure proper study design under quasi‐experimental settings, including propensity score analysis, before‐after‐control‐impact studies, regression discontinuity design, and instrumental variables. Causal diagrams represent a unified approach to variable selection across methodologies and should be routinely applied in ecology research with causal implications.