Causal Inference Framework for Ocean Microbial Community Responses to Warmer Temperature
MCML Authors
Christian Müller
Prof. Dr.
Core PI
Abstract
Christian Müller
Prof. Dr.
Core PI
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
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.Authors
M. V. Tran • C. L. MüllerLinks
URLResearch Area
BibTeXKey: TM25