# Bioresearch Causal Inference

> Run two-sample Mendelian randomization via the BioResearch Agent causal workflow (IVW estimation, leave-one-out sensitivity, scatter / funnel plots). Use when the user asks to test a causal effect between an exposure and an outcome (e.g., BMI → Type 2 Diabetes) using genetic instruments.

- Skill: `alim430/bioresearch-causal-inference` (Agent Skill)
- Install (CLI): `npx skillmds@latest add alim430/bioresearch-causal-inference`
- Raw SKILL.md: https://api.skillmd.com/api/skills/alim430/bioresearch-causal-inference/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Alim430 (https://skillmd.com/u/alim430)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/alim430/bioresearch-causal-inference

---


# BioResearch Agent — Causal Inference (MR) Skill

## Capability

Runs a reproducible two-sample Mendelian randomization: GWAS summary statistics (simulated by
default) → genome-wide significant SNP instruments → IVW causal estimate → leave-one-out
sensitivity → scatter / funnel plots. Returns a quantitative causal estimate, not a claim.

## Run

```bash
bioresearch run causal --exposure BMI --outcome "Type 2 Diabetes"
```

## Outputs (in `outputs/causal/`)

- `causal_ivw_results.csv` — IVW estimate, SE, p-value
- `causal_loo_results.csv` — leave-one-out sensitivity
- `causal_mr_scatter.png` — MR scatter plot (IVW slope labeled)
- `causal_mr_funnel.png` — funnel plot
- `causal_interpretation.txt` — interpretation notes

## Note

This skill dispatches to the framework's `causal` workflow. It adds **no analysis of its own**;
all MR statistics run in the workflow modules. By default uses simulated GWAS data (no external
credentials required).