Assessing the adoption of Causal Language and Methodology in Human Microbiome Studies: A Methodological Review

Background Microbiome research seeks to clarify how microbial communities influence human health. Although etiological research paradigms are evolving in the bio-medical sciences, many microbiome studies continue to rely on association-based methods that detect statistical patterns but are limited identifying causal mechanisms needed to inform clinical or public health interventions. This methodological review evaluates the extent to which modern causal inference approaches have been adopted in human microbiome studies and identifies persistent challenges to their broader implementation. Methods We systematically reviewed human microbiome studies published between 2019 and 2024 that examined links between the microbiome and health outcomes, or between exposures/interventions and microbiome composition, across ten high-impact journals identified using the Scimago Journal and Country Ranking. Eligible studies were retrieved from PubMed using a predefined search strategy. Two reviewers independently screened titles, abstracts and full texts and extracted data on study design, sampling, analytical framework, confounding control, effect size reporting, and the use of causal language. Analyses were performed using standardized extraction templates. Results Across 205 included studies, adoption of causal inference approaches in microbiome research remains limited. Only 15% of studies used designs or analytical strategies capable of approximating causal effects—12% were randomized controlled trials and 3% were observational studies employing formal causal inference methods. Longitudinal designs were common (45%). However, 30% of studies did not address confounding, and more than 40% did not report intervention-relevant or clinically actionable effect sizes. Studies making stronger causal claims were also more likely to propose intervention-relevant recommendations, regardless of the underlying study design. Conclusion The limited use of rigorous causal inference approaches remains a key barrier to producing actionable evidence in microbiome research. Greater adoption of principled confounding control, improved use of mediation and effect-modification frameworks, and more consistent reporting of interpretable effect sizes are necessary to strengthen causal claims. Advancing methodological standards and promoting interdisciplinary collaboration will be essential for translating microbiome findings into clinically meaningful insights.