Introduction to Causal Inference Methods in Nutritional Research
A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition Methodology & Assessment".
Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 26783
Special Issue Editor
Special Issue Information
Dear Colleagues,
A common goal of nutritional research is to identify causal effects of nutritional exposures on health outcomes, and to estimate their magnitude. However, the data used for nutritional research is frequently observational, reflecting ethical and logistical challenges with randomly assigning nutritional exposures and the relative ease of collecting such exposures via participant self-report.
Observational data pose important challenges for causal inference, with effect estimates potentially biased by confounding (measured or unmeasured), measurement error and selection bias. Various statistical and epidemiological methods support causal inference from observation data, including causal diagrams, instrumental variable analysis, propensity scores and quantitative bias analyses. However, these methods also have underlying assumptions, require specific expertise and may not successfully remove all bias.
In this Special Issue, we highlight the value of causal inference methodology in nutritional research. We welcome reviews explaining causal inference methods for a nutritional audience, research articles showcasing the use of causal inference methods in nutritional research, and commentaries.
Dr. Elizabeth Holliday
Guest Editor
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Keywords
- nutrition
- obesity
- observational study
- causal inference
- directed acyclic graph (DAG)
- mediation
- propensity score
- bias analysis
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