The vaginal microbiome is a dynamic ecosystem vital to women’s reproductive health, yet its regulatory mechanisms remain poorly understood. This study aims to identify keystone (positive regulator) and gatekeeper (negative regulator) species to reveal microbial interactions and their roles in maintaining ecosystem stability. By finding the connectivity of species within their regulatory network and using causal inference methods such as Average Causal Effect (ACE), the study quantifies interaction patterns, captures the strength and directionality of interactions, and evaluates species’ influence on community dynamics. To validate these causal insights, microbial population dynamics are simulated using generalized Lotka-Volterra models with noise to mimic ecological perturbations. By comparing synthetic interaction patterns with causal models derived from real-world data, the study seeks to assess the robustness and interpretability of inferred microbial interactions. The research aims to provide insights that could inform targeted therapeutic strategies and improve diagnostics, contributing to a deeper understanding of microbial community dynamics and advancing knowledge in women’s health.

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