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Ailer, E. ; Müller, C.L. ; Kilbertus, N.

Instrumental variable estimation for compositional treatments.

Sci. Rep. 15:5158 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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 structure of the compositional sample space into account while still yielding scientifically interpretable results. In a comparative analysis on synthetic and real microbiome data we show the advantages and limitations of our proposal. We posit that our analysis provides a useful framework and guidance for valid and informative cause-effect estimation in the context of compositional data.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Causality ; Cause-effect Estimation ; Compositional Data ; Instrumental Variable ; Microbial Diversity; Statistical-analysis; Regression; Diversity; Selection; Models
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 2045-2322
e-ISSN 2045-2322
Journal Scientific Reports
Quellenangaben Volume: 15, Issue: 1, Pages: , Article Number: 5158 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-530003-001
G-503800-001
Grants Helmholtz Association under the joint research school "Munich School for Data Science-MUDS"
Projekt DEAL
DOI 10.1038/s41598-025-89204-9
WOS ID 001420050200004
PubMed ID 39934389
Erfassungsdatum 2025-04-08
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