AI Applications for Causal Inference in Microbiome Research: Moving Beyond Correlation.

Recent advances in microbiome science have revealed extensive associations between microbial communities and host health; however, the causal mechanisms driving these relationships remain incompletely understood. This Research Topic focuses on the application of artificial intelligence (AI) and computational modeling to move beyond correlation and toward establishing causation in human, animal, plant, and environmental microbiomes.We welcome studies that integrate multi-omics datasets with AI-driven analyses, as well as those that include experimental or functional validation to link predictive models with biological mechanisms. Submissions may include original research, reviews, methods, and perspectives employing approaches such as Bayesian networks, machine learning, deep learning, causal inference frameworks, and simulation-based modeling to dissect microbial interactions that influence host physiology, immunity, metabolism, and disease susceptibility.We particularly encourage contributions addressing key challenges in microbiome data science—such as dataset heterogeneity, bias correction, model interpretability, scalability, and the integration of temporal or spatial microbial dynamics.This Research Topic aims to serve as a collaborative platform for computational microbiologists, bioinformaticians, systems biologists, and clinical researchers working to develop and apply AI tools that uncover actionable causal insights within complex microbiome ecosystems. Topic editors: Eman Mohammad Khalaf, Mohamed Mysara, Mohsen Yoosefzadeh Najafabadi Source: https://www.frontiersin.org/research-topics/74455/ai-applications-for-causal-inference-in-microbiome-research-moving-beyond-correlation