Beyond just correlation: causal machine learning for the microbiome, from prediction to health policy with econometric tools
Abstract
The human microbiome is increasingly recognized as a key mediator of health and disease, yet translating microbial associations into actionable interventions remains challenging. This review synthesizes advances in machine learning (ML) and causal inference applied to human microbiome research, emphasizing policy-relevant applications. Explainable ML approaches, have identified microbial drivers, guiding targeted strategies. Econometric tools, including instrumental variables, difference-in-differences, and panel data models, provide robust frameworks for validating causal relationships, while hybrid methods like Double Machine Learning (Double ML) and Deep Instrumental Variables (Deep IV) address high-dimensional and non-linear effects, enabling precise evaluation of microbiome-mediated interventions. Policy translation is further enhanced by federated learning, standardized analytical pipelines, and model visualization frameworks, which collectively improve reproducibility, scalability, and data privacy compliance. By integrating predictive power with causal rigor, microbiome research can move beyond observational associations to generate interventions that are biologically grounded, clinically actionable, and policy-ready. This roadmap provides a blueprint for translating mechanistic microbial insights into real-world health solutions, emphasizing interdisciplinary collaboration, standardized reporting, and evidence-based policymaking.
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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