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Deterministic variables are variables that are fully explained by one or moreparent variables. They commonly arise when a variable has been algebraicallyconstructed from one or more parent variables, as with composite variables, andin compositional data, where the 'whole' variable is determined from its'parts'.  This article introduces how deterministic variables may be depicted withindirected acyclic graphs (DAGs) to help with identifying and interpreting causaleffects involving tautological associations, compositional data, and compositevariables. We propose a two-step approach in which all variables are initiallyconsidered, and an explicit choice is then made whether to focus on thedeterministic variable(s) or the determining parents.  Depicting deterministic variables within DAGs bring several benefits. It iseasier to identify and avoid misinterpreting tautological associations, i.e.,self-fulfilling associations between variables with shared algebraic parentvariables. In compositional data, it is easier to understand the consequencesof conditioning on the 'whole' variable, and correctly identify total andrelative causal effects. For composite variables, it encourages greaterconsideration of the target estimand and greater scrutiny of the consistencyand exchangeability assumptions.  DAGs with deterministic variables are a useful aid for planning andinterpreting analyses involving tautological associations, compositional data,and/or composite variables.

Depicting deterministic variables within directed acyclic graphs (DAGs):  An aid for identifying and interpreting causal effects involving tautological  associations, compositional data, and composite variables