Causal discovery for the microbiome.

Area of Science:

• Microbiome research
• Causal inference
• Metagenomics

Background:

• The human microbiome is linked to complex diseases, but establishing causality is challenging.
• Koch's postulates highlight the need for causative reasoning in microbe-disease relationships.
• Observational microbiome data presents difficulties for causal discovery.

Purpose of the Study:

• To demonstrate the capability of current causal inference methods for metagenomic data.
• To explore how flexible causal structures can enhance causal reasoning in microbiome studies.
• To guide microbiome research in leveraging recent causal discovery advancements.

Main Methods:

• Review and application of existing causal structure learning algorithms.
• Analysis of metagenomic data to infer causal effects.
• Exploration of novel, more flexible causal structure frameworks.

Main Results:

• Existing causal discovery methods show potential for inferring effects from metagenomic data.
• More flexible causal structures present new avenues for causal reasoning.
• Metagenomic data analysis benefits from advanced causal inference techniques.

Conclusions:

• Microbiome research can significantly benefit from recent developments in causal discovery.
• Adopting advanced causal inference tools can strengthen the understanding of microbe-disease relationships.
• Learning from applications of causal discovery in other fields is recommended.