ArticleAmerican journal of epidemiology2025
Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data.
Article in American journal of epidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Nonrandom Selection With and Without Bias Due to Selecting on an Exposure.Epidemiology (Cambridge, Mass.) · 2026Article
- Considerations for the Analysis of Urinary Environmental Chemical Concentrations During Pregnancy.Epidemiology (Cambridge, Mass.) · 2026Article
- Causal Inference Tools for Pharmacovigilance: Using Causal Graphs to Identify and Address Biases in Disproportionality Analysis.Drug safety · 2026Article
- What Are We Estimating? Revisiting Standard Nutritional Models Through the Target Trial Framework.American journal of epidemiology · 2026Article
- Quantifying excess TB prevalence among people living with HIV on ART in the universal ART era: Evidence from a TB prevalence survey in Zambia and South Africa.PLOS global public health · 2026Article
- Article
- Making DAGs even more useful: using augmented causal diagrams to depict counterfactual, study design, measurement, analytical, and interventional features.International journal of epidemiology · 2025Article
- A causal framework for the drivers of animal social network structure.PLoS computational biology · 2025Article
- Depicting patient-reported outcome measures within directed acyclic graphs: practice and implications for causal reasoning.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Composite variable bias: causal analysis of weight outcomes.International journal of obesity (2005) · 2025Article
- A comparison of methods for analysing compositional data with fixed and variable totals: a simulation study using the examples of time-use and dietary data.BMC medical research methodology · 2025Article
- Simulating hierarchical data to assess the utility of ecological versus multilevel analyses in obtaining individual-level causal effects.BMC medical research methodology · 2025Article
- Depicting deterministic variables within directed acyclic graphs: an aid for identifying and interpreting causal effects involving derived variables and compositional data.American journal of epidemiology · 2025Article
- Association and substitution analyses of dietary sugars, starch and fiber for indices of body fat and cardiometabolic risk- a NoHoW sub-study.European journal of nutrition · 2025Article
- The marginality principle revisited: Should "higher-order" terms always be accompanied by "lower-order" terms in regression analyses?Biometrical journal. Biometrische Zeitschrift · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Deterministic variables are variables that are functionally determined by one or more parent variables. They commonly arise when a variable has been functionally created from one or more parent variables, as with derived variables, 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 derived variables and/or compositional data. We propose a 2-step approach in which all variables are initially considered, and a choice is made as to whether to focus on the deterministic variable or its determining parents. Depicting deterministic variables within DAGs brings several benefits. It is easier to identify and avoid misinterpreting tautological associations, that is, self-fulfilling associations between deterministic variables and their parents, or between sibling variables with shared parents. In compositional data, it is easier to understand the consequences of conditioning on the "whole" variable and to correctly identify total and relative causal effects. For derived variables, it encourages greater consideration of the target estimand and greater scrutiny of the consistency and exchangeability assumptions. DAGs with deterministic variables are a useful aid for planning and interpreting analyses involving derived variables and/or compositional data.
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