While associations between dietary factors and disease outcomes are frequently observed in epidemiological research, such associations do not inherently imply causation, creating significant challenges for translating findings into public health and nutrition policies. This paper critically analyses and selectively reviews key studies to provide a nuanced perspective on the application of causal inference methods in nutritional epidemiology. Estimating causal effects is essential for informing dietary guidelines; however, randomised controlled trials (RCTs), considered the gold standard for causal inference, are often impractical or unavailable for many dietary exposures due to cost, duration, ethical concerns, or feasibility constraints. Consequently, observational data are frequently used to guide nutrition recommendations. Applying causal inference methods to these data presents unique challenges, including confounding, measurement error, and selection bias, which can distort effect estimates. Triangulation of evidence from multiple study designs—basic science, RCTs, and observational epidemiology—offers a pathway to strengthen causal claims, yet findings from non-human experiments or highly controlled trial environments may not always generalise to real-world populations. Furthermore, meta-analyses that combine heterogeneous study estimates, without careful alignment of exposure definitions, populations, and analytical strategies, may lack a clear causal interpretation and risk misleading conclusions. This review emphasises the importance of clearly formulating causal questions using tools such as directed acyclic graphs (DAGs), selecting appropriate statistical methods (e.g., propensity score methods, marginal structural models, g-formula), and conducting sensitivity analyses to assess robustness to unmeasured confounding. We also highlight the need for greater transparency in reporting methodological decisions and underlying assumptions, as well as careful interpretation of results within the context of nutrition-specific challenges such as dietary complexity, multi-collinearity of exposures, and long latency periods for disease development. Overall, this work underscores that improving causal inference in nutritional epidemiology requires both methodological refinement and interpretative caution. By applying rigorous, transparent, and contextually appropriate causal methods to observational nutrition data, researchers can provide stronger and more policy-relevant evidence to guide dietary guidelines and interventions aimed at improving population health.
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