The reproducibility crisis in causal microbiome research necessitates robust validation frameworks. Current studies often face inconsistent validation methods, limited interpretability, and a lack of standardized reporting, creating a gap in reliable causal inference. This systematic review evaluates over 60 peer-reviewed studies published between 2015 and 2024 to: (1) establish benchmarking standards leveraging synthetic data and biological plausibility assessments; (2) compare advanced causal machine learning (ML) methodologies, including Double/Debiased ML, Deep Instrumental Variables (Deep IV), and Directed Acyclic Graphs (DAGs), in their application to microbiome-host systems; and (3) propose the STROBE-CML (Strengthening the Reporting of Observational Studies in Epidemiology–Causal Machine Learning) guidelines to standardize reporting practices. We emphasize critical innovations such as federated validation pipelines and time-series causal discovery frameworks that address these gaps by facilitating scalable, privacy-preserving, and reproducible inference across heterogeneous cohorts. A decision support tool is introduced to guide researchers in selecting appropriate causal ML approaches based on data structure, research question, and computational constraints. By synthesizing methodological advances with rigorous validation paradigms, this review provides a roadmap for generating reliable, biologically interpretable, and clinically translatable causal claims in microbiome science.
Khelfaoui et al. (Wed,) studied this question.