Mediation Analysis

What the source does

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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/causal-genomics/mediation-analysisThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a…

---
name: bio-causal-genomics-mediation-analysis
description: Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.
tool_type: mixed
primary_tool: mediation
---

## Version Compatibility

Reference examples tested with: R 4.3+, mediation 4.5.0+, CMAverse 0.1.0+ (GitHub `BS1125/CMAverse`), HIMA >= 2.3.0 (GitHub `YinanZheng/HIMA`; archived from CRAN 2026-07), bama 1.3+, causalweight 1.0.5+ (medDML), MVMR 0.4+, TwoSampleMR 0.6+, EValue 4.1+, gesttools 1.3+, ipw 1.0.11+.

Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- HIMA must be pinned at `>= 2.3.0` for the code patterns below; the formula interface `hima(formula, data.pheno, data.M, mediator.type, penalty, ...)` was introduced in 2.3.0. On HIMA 2.2.x the classic engine is the top-level `hima(X, Y, M, COV.XM=, COV.MY=, Y.family=, penalty=)` (positional order X, Y, M) with no formula interface; HIMA 2.2.x is NOT API-compatible with the examples here.
- In 2.3+ the classic engine is `hima_classic(X, M, Y, COV.XM=, COV.MY=, Y.type=)` (positional order X, M, Y; outcome type via `Y.type`, not `Y.family`); use it only to reproduce the original 2016-2021 SIS+penalty pipelines.

If code throws an error, introspect the installed package (`?hima`, `args(cmest)`) and adapt the example to match the actual API rather than retrying.

# Mediation Analysis

**"Does expression of GENE_X mediate the SNP-to-disease effect?"** -> Decompose the total effect of a treatment (genotype, exposure) on an outcome into direct and indirect paths through one or more mediators, with explicit handling of exposure-mediator interaction, sensitivity to unmeasured confounding, and high-dimensional mediator screening.

- R (single-mediator, observational, sequential-ignorability assumed): `mediation::mediate(med_model, out_model, treat='X', mediator='M', boot=TRUE, sims=5000)`
- R (4-way decomposition with exposure-mediator interaction): `CMAverse::cmest(...EMint=TRUE, estimation='paramfunc', inference='bootstrap', nboot=1000)`
- R (high-dimensional / EWAS mediators): `HIMA::hima(Y ~ X + covariates, data.pheno, data.M, mediator.type='gaussian', penalty='DBlasso')` (modern v2.3+ formula interface)
- R (MR-based mediation): two-step `TwoSampleMR` with independent instruments OR `MVMR::ivw_mvmr` for joint direct effect
- R (doubly-robust double-ML): `causalweight::medDML(y, d, m, x)`

Sequential ignorability (no unmeasured confounder of treatment-mediator, mediator-outcome, treatment-outcome) is the single load-bearing assumption of observational mediation and is fundamentally untestable. Every report should include a sensitivity result (Imai's rho via `medsens()` or a mediational E-value).

## Algorithmic Taxonomy

| Method | Framework | Handles E-M interaction | High-D mediators | Min n | Fails when |
|--------|-----------|--------------------------|------------------|-------|------------|
| Baron-Kenny (1986) | Additive regression-based product/difference | No | No | ~100 | Any non-linearity, interaction, or binary outcome; deprecated for causal inference |
| Imai mediation R (Imai 2010 Psychol Methods) | Counterfactual ACME/ADE with bootstrap | Yes (via interaction term in outcome model) | No | ~200 | Sequential ignorability violated; exposure-induced M-Y confounder; rare binary outcome with logistic outcome model |
| VanderWeele 4-way (VanderWeele 2014 Epidemiology 25:749; 2015 OUP) | CDE + PIE + INTref + INTmed decomposition | Native | No | ~300 | Without interaction term reduces to standard mediation; binary outcome needs rare-disease assumption |
| CMAverse (Shi 2021 Epidemiology 32:e20) | 6 estimators: regression (`rb`), weighting (`wb`), IORW (`iorw`), natural effect models (`ne`), MSM (`msm`), g-formula (`gformula`) | Yes | No (single M, or M-vector) | ~300 | Estimator-specific; `wb` fails with rare exposure; `msm` needs censoring weights for survival |
| HIMA1 / hima_classic (Zhang 2016 Bioinformatics 32:3150) | SIS screen by beta (M->Y) + MCP penalty | No | Yes (up to ~10k) | ~150 + p>>n | Misses mediators with strong alpha and weak beta; screening-step false-negatives |
| HIMA2 / hima (Perera 2022 BMC Bioinformatics 23:296) | SIS screen by alpha*beta (indirect effect) + de-biased Lasso (DBlasso) | No (linear by default) | Yes | ~150 | Outcome family limited (gaussian/binomial); HIMA-Cox for survival; HIMA-Pois for count |
| HILAMA (Wang et al 2025) | High-D mediation with latent confounders | No | Yes (>= 100k) | ~500 | Newer; benchmarks evolving; requires latent-factor specification |
| BAMA (Song 2020 Biometrics) | Bayesian high-D continuous shrinkage | No | Yes (~5k) | ~200 | Slow MCMC; prior sensitivity for very weak mediators |
| Two-step MR / network MR (Burgess 2015 IJE 44:484) | IV-based at each step with INDEPENDENT instruments | Implicit (no interaction modeling) | One mediator at a time | Large summary-stat samples | Same SNP used for E and M (violates exclusion); horizontal pleiotropy; Steiger reversal of M->E direction |
| MVMR-mediation (Carter & Sanderson 2021 Eur J Epidemiol 36:465-478) | Total minus direct via MVMR | Implicit | Single mediator | Large GWAS samples for both E and M | Conditional F < 10 for either exposure; correlated instruments |
| medDML (Farbmacher 2022 Econometrics J 25:277) | Double-debiased ML, doubly-robust | Limited (depends on learner) | Moderate (sparsity-friendly) | ~500 | Severe overlap violations; cross-fitting variance with small n |

Methodology evolves; verify against the cur

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git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Mediation Analysis

• github.com/GPTomics/bioSkills/tree/main/causal-genomics/mediatio — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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