Causal Inference Framework for Ocean Microbial Community Responses to Warmer Temperature

MCML Authors

Christian Müller

Prof. Dr.

Principal Investigator

Biomedical Statistics and Data Science

Abstract

Understanding how ocean temperature influences microbial communities is critical for forecasting ecological responses to climate change. This paper outlines a framework for applying the Rubin Causal Model (RCM) to observational amplicon sequencing data using matching methods. The aim is to estimate the causal effect of temperature on microbial taxa, while controlling for confounding environmental variables. This methodology offers a transparent, interpretable, and simulation-free way to extract causal signals from complex ecological datasets.

inproceedings TM25

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Climate Change AI @NeurIPS 2025

Workshop on Tackling Climate Change with Machine Learning at the 39th Conference on Neural Information Processing Systems. San Diego, CA, USA, Nov 30-Dec 07, 2025.

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Authors

M. V. Tran • C. L. Müller

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Links

URL

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Research Area

 C2 | Biology