Keystone taxa in ecological communities are native taxa that play an especially important role in the stability of their ecosystem. However, we still lack an effective framework for identifying these taxa from the available high-throughput sequencing without the notoriously difficult step of reconstructing the detailed network of inter-specific interactions. In addition, while most microbial interaction models assume pair-wise relationships, it is yet unclear whether pair-wise interactions dominate the system, or whether higher-order interactions are relevant. Here we propose a top-down identification framework, which detects keystones by their total influence on the rest of the taxa. Our method does not assume a priori knowledge of pairwise interactions or any specific underlying dynamics and is appropriate to both perturbation experiments and metagenomic cross-sectional surveys. When applied to real high-throughput sequencing of the human gastrointestinal microbiome, we detect a set of candidate keystones and find that they are often part of a keystone module – multiple candidate keystone species with correlated occurrence. The keystone analysis of single-time-point cross-sectional data is also later verified by the evaluation of two-time-points longitudinal sampling. Our framework represents a necessary advancement towards the reliable identification of these key players of complex, real-world microbial communities.