  Microbiome interventions provide valuable data about microbial ecosystem
structure and dynamics. Despite their ubiquity in microbiome research, few
rigorous data analysis approaches are available. In this study, we extend
transfer function-based intervention analysis to the microbiome setting,
drawing from advances in statistical learning and selective inference. Our
proposal supports the simulation of hypothetical intervention trajectories and
False Discovery Rate-guaranteed selection of significantly perturbed taxa. We
explore the properties of our approach through simulation and re-analyze three
contrasting microbiome studies. An R package, mbtransfer, is available at
https://go.wisc.edu/crj6k6. Notebooks to reproduce the simulation and case
studies can be found at https://go.wisc.edu/dxuibh and
https://go.wisc.edu/emxv33.
