Instrumental variable estimation for compositional treatments.

ABSTRACT: Many scientific datasets are compositional in nature. Important biological examples include species abundances in ecology, cell-type compositions derived from single-cell sequencing data, and amplicon abundance data in microbiome research. Here, we provide a causal view on compositional data in an instrumental variable setting where the composition acts as the cause. First, we crisply articulate potential pitfalls for practitioners regarding the interpretation of compositional causes from the viewpoint of interventions and warn against attributing causal meaning to common summary statistics such as diversity indices in microbiome data analysis. We then advocate for and develop multivariate methods using statistical data transformations and regression techniques that take the special struct

SUBMITTER: Ailer E 

PROVIDER: S-EPMC11814326 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature