BACKGROUND: Deterministic variables are variables that are fully explained by one or more parent variables. They commonly arise when a variable has been algebraically constructed from one or more parent variables, known as transformed variables and composite variables respectively, and in compositional data, where the 'whole' variable is determined from its 'parts'. This article introduces how deterministic variables may be depicted within directed acyclic graphs (DAGs) to help with identifying and interpreting causal effects involving tautological associations, compositional data, and composite variables. DEVELOPMENT: This article proposes a two-step approach to the handling of deterministic variables when identifying and interpreting causal effects. First, a 'full' DAG is drawn that includes all deterministic variables and all determining parents. For clarity, deterministic variables should be depicted with double-outlined nodes and all their incoming arcs should be double-lined. Next, an explicit choice should be made whether to focus on the deterministic variable(s) or the determining parents. APPLICATION: Depicting deterministic variables within DAGs bring several benefits. It is easier to identify and avoid misinterpreting tautological associations, i.e., self-fulfilling associations between variables with shared algebraic parent variables. In compositional data, it is easier to understand the consequences of conditioning on the 'whole' variable, and in turn correctly identify total and relative causal effects. For composite variables, it encourages greater consideration of the target estimand and whether the consistency and exchangeability assumptions can be satisfied. CONCLUSION: DAGs with deterministic variables are a useful aid for planning and/or interpreting analyses involving transformed variables, compositional data, and/or composite variables.

翻译：确定性变量 : 确定性变量是由一个或多个母变量充分解释的变量 。 通常, 当变量是从一个或数个母变量( 分别称为变变变变量和复合变量)和组成数据中进行代数构建的, 其“ 全”变量由“ parts” 来决定 。 本条介绍了如何在定向循环图( DAGs) 中描述确定性变量, 以帮助识别和解释由一种或多种母变量、 组成数据和复合变量组成的因果关系。 开发: 本条提出在确定和解释因果关系效果时, 处理确定性变量的两步方法。 首先, 绘制“ 完整” 数据组, 包括所有确定性变量和所有决定性父母的数据组。 关于清晰性, 确定性变量组应该用双向节点来描述, 所有流入的弧性变量组( DAGs) 应该双向地描述。 明确选择, 确定性变量组和确定性变量组的稳定性变量组和确定性。 应用: 描述DAG/ 解释性变量组, 解释性变变式分析是比较容易, 解释性变式分析 。, 解 和变式的变量组会 解释性分析,, 解释性变式组会, 和变式组会, 解释性分析, 解释性变式会 解释性分析, 解释性分析,, 解释性 解释性,,, 和变式 解释性分析, 解释性 解释性 解释性,, 解释性分析,,, 解释性变式, 和 解释性 解释性 解释性, 和 解释性, 解释性,,, 解释性,, 解释性,,,,,, 解释性,,,,,,,, 解释性, 解释性, 解释性, 解释性, 解释性, 解释性, 解释性,, 和,,,,, 解释性, 解释性, 解释性, 解释性, 解释性, 解释性, 解释性, 解释性, 和