From association to causation: a decision-aware framework for reproducible biomarker discovery and precision intervention design in the human gut microbiome

Human gut microbiome research has generated many disease associations, yet few translate into clinical applications. A central obstacle is not a lack of data, but the limited integration of causal reasoning, as most studies report correlations without establishing directionality, confounding control, or mechanistic evidence. We propose a unified causal inference framework that integrates directed acyclic graphs, Mendelian randomization, double machine learning, mediation analysis, and tests of causal reversibility into a single decision-aware workflow. Unlike prior applications of these tools in isolation, our framework explicitly separates assumption mapping, causal identification, effect estimation, and mechanistic interpretation, introducing "assumption guardrails" that constrain interpretation at each stage and prevent overinterpretation of observational findings. Using a colorectal cancer case study with public metagenomic data, we demonstrate how the framework operates under real-world constraints, transforming observational associations into testable, mechanism-based hypotheses. The contribution is architectural in that it organizes existing tools into a disciplined, integrated pipeline that clarifies the strength of evidence at each stage. This operational blueprint provides a reproducible path from correlation to causation in microbiome research and toward precision interventions.