Systematic Review of Causal Machine Learning in Microbiome Research

Issam Khelfaoui's systematic review evaluates over 60 peer-reviewed studies published from 2015 to 2024. It compares causal machine learning methodologies and proposes reporting guidelines for microbiome-host systems.

Use Cases

• Analyze the application of Double/Debiased ML, Deep IV, and DAGs across the reviewed studies to compare methodological performance.
• Extract validation frameworks, including synthetic data and biological plausibility assessments, for benchmarking causal inference models.
• Use the proposed STROBE-CML guidelines to standardize reporting practices in causal microbiome research.

Strengths

• Synthesizes findings from over 60 peer-reviewed studies.
• Covers methodological innovations published between 2015 and 2024.
• Proposes specific reporting guidelines (STROBE-CML) to address standardization gaps.

Limitations

• The dataset is a 16.8 KB document, containing only textual review content without structured data tables.
• No primary research data, sample data, or columnar features are included for direct analysis.
• Scope is limited to a review of existing literature, not original experimental or observational data.

Provenance

Source

Issam Khelfaoui via figshare

Collection Method

Systematic review of published literature.

Time Range

Studies published between 2015 and 2024.

Freshness

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Geography

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