dag-development
Develop causal diagrams (DAGs) from social-science research questions and literature, then render publication-ready figures using Mermaid, R, or Python.
What this skill does
# 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 1. **Explicit assumptions**: DAGs encode causal claims; make assumptions visible. 2. **Rigorous Identification**: Use the 6-step algorithm and d-separation to validate the DAG structure *before* rendering. 3. **Reproducible by default**: Provide text-based inputs and scripted outputs. 4. **Exportable assets**: Produce SVG/PNG (and PDF where possible). 5. **Tool choice**: Offer three rendering paths with tradeoffs. 6. **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 ```
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