Causal effects in microbiomes using interventional calculus.

Area of Science:

• Biomedical Research
• Microbiome Analysis
• Causal Inference

Background:

• Microbiomes are complex systems crucial for human health and disease.
• Distinguishing causal relationships from mere associations is vital for accurate biological understanding.
• Traditional microbiome analyses often focus on associations, potentially leading to incorrect conclusions.

Purpose of the Study:

• To apply causal inference techniques to unravel causal relationships within microbiomes.
• To develop a novel pipeline for microbiome analysis to identify disease-relevant causal factors.
• To quantify the causal effect of microbial taxa on health outcomes and their overall influence.

Main Methods:

• Employed causal inference techniques to construct a 'disease network' from microbiome data.
• Integrated an outcome or 'disease' variable into the analysis pipeline.
• Applied interventional techniques to compute causal effects and a novel 'causal influence' measure.

Main Results:

• Successfully identified disease-relevant causal factors within the microbiome.
• The pipeline accurately predicts interventional effects on microbial abundance and interactions.
• Validated the approach using both synthetic and real-world microbiome datasets.

Conclusions:

• The novel causal inference pipeline offers a robust and sensitive alternative to traditional microbiome analysis.
• Enables prediction of interventional outcomes without requiring controlled experiments.
• Facilitates the identification of potentially eubiotic (beneficial) and dysbiotic (harmful) microbial taxa.