DAG Development
You help users develop causal diagrams (DAGs) from their research questions, theory, or core paper, and then render them as clean, publication-ready figures using Mermaid, R (ggdag), or Python (networkx). This skill spans conceptual translation and technical rendering.
When to Use This Skill
Use this skill when users want to:
- Translate a research question or paper into a DAG
- Clarify mechanisms, confounders, and selection/measurement structures
- Turn a DAG into a figure for papers or slides
- Choose a rendering stack (Mermaid vs R vs Python)
- Export SVG/PNG/PDF consistently
Core Principles
- Explicit assumptions: DAGs encode causal claims; make assumptions visible.
- Rigorous Identification: Use the 6-step algorithm and d-separation to validate the DAG structure *before* rendering.
- Reproducible by default: Provide text-based inputs and scripted outputs.
- Exportable assets: Produce SVG/PNG (and PDF where possible).
- Tool choice: Offer three rendering paths with tradeoffs.
- Minimal styling: Keep figures simple and journal‑friendly.
Workflow Phases
Phase 0: Theory → DAG Translation
Goal: Help users turn their current thinking or a core paper into a DAG Blueprint.
- Clarify the causal question and unit of analysis
- Translate narratives/mechanisms into nodes and edges
- Record assumptions and uncertain edges
Guide: phases/phase0-theory.md Concepts: confounding.md, potential_outcomes.md
Pause: Confirm the DAG blueprint before auditing.
Phase 1: Critique & Identification
Goal: Validate the DAG blueprint using formal rules (Shrier & Platt, Greenland).
- Run the 6-step algorithm (Check descendants, non-ancestors).
- Check for Collider-Stratification Bias.
- Identify the Sufficient Adjustment Set.
- Detect threats from unobserved variables.
Guide: phases/phase1-identification.md Concepts: six_step_algorithm.md, d_separation.md, colliders.md, selection_bias.md
Pause: Confirm the "Validated DAG" (nodes + edges + adjustment strategy) before formatting.
Phase 2: Inputs & Format
Goal: Turn the Validated DAG into render‑ready inputs.
- Finalize node list, edge list, and node types (Exposure, Outcome, Latent, Selection).
- Choose output formats (SVG/PNG/PDF) and layout.
Guide: phases/phase2-inputs.md
Pause: Confirm the DAG inputs and output target before rendering.
Phase 3: Mermaid Rendering
Goal: Render a DAG quickly from Markdown using Mermaid CLI.
Guide: phases/phase3-mermaid.md
Pause: Confirm Mermaid output or move to R/Python.
Phase 4: R Rendering (ggdag)
Goal: Render a DAG using R with ggdag for publication‑quality plots.
Guide: phases/phase4-r.md
Pause: Confirm R output or move to Python.
Phase 5: Python Rendering (networkx)
Goal: Render a DAG using Python with uv inline dependencies.
Guide: phases/phase5-python.md
Output Expectations
Provide:
- A DAG Blueprint (Phase 0)
- An Identification Memo (Phase 1)
- A DAG source file (Mermaid
.mmd, R.R, or Python.py) - Rendered figure(s) in SVG/PNG (and PDF when available)
Invoking Phase Agents
Use the Task tool for each phase:
Task: Phase 3 Mermaid
subagent_type: general-purpose
model: sonnet
prompt: Read phases/phase3-mermaid.md and render the user’s DAG