# Causal Modeling

> Campaign for building causal models — identify variables, map mechanisms, collect evidence, analyze interventions, validate models. Produces causal graphs in the wiki vault.

- Skill: `yogsoth-ai/causal-modeling-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add yogsoth-ai/causal-modeling-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yogsoth-ai/causal-modeling-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: yogsoth-ai (https://skillmd.com/u/yogsoth-ai)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/yogsoth-ai/causal-modeling-2

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# Causal Modeling

Build causal models for research domains. Identifies variables, maps causal mechanisms, collects supporting evidence, analyzes potential interventions, and validates the resulting causal graph.

## Manifest

| Level | Count | Skills |
|-------|-------|--------|
| Strategy | 5 | variable-identification, mechanism-mapping, evidence-collection, intervention-analysis, model-validation |
| Tactic | 3 | counterfactual-reasoning, evidence-weighing, feedback-loop-detection |
| SOP | 10 | variable-page-creation, mechanism-edge-creation, evidence-linking, contradiction-flagging, confidence-scoring, intervention-page-creation, loop-documentation, model-gap-detection, causal-chain-query, validation-report |

## Budget Table

| Metric | Small | Medium | Large |
|--------|-------|--------|-------|
| Variables identified | 8 | 20 | 40 |
| Causal edges created | 15 | 40 | 80 |
| Evidence pages linked | 10 | 30 | 60 |
| Interventions analyzed | 2 | 5 | 10 |
| Feedback loops documented | 1 | 3 | 6 |

## Strategy Sequence (Reference, Not Prescription)

1. **variable-identification** — identify key variables in the causal system
2. **mechanism-mapping** — map causal mechanisms between variables
3. **evidence-collection** — gather evidence supporting/refuting causal claims
4. **intervention-analysis** — analyze what happens when variables are manipulated
5. **model-validation** — validate the causal model for consistency and completeness

## MCP Tools Used

- `vault_search` — find existing variables and mechanisms
- `vault_add_edge` — create causal edges (derived_from, supported_by, contradicts)
- `vault_query_graph` — trace causal chains
- `vault_graph_stats` — assess model coverage
- `vault_lint` — validate structural integrity

## Context-Management

<HARD-GATE>
- Call `context-init` at campaign start
- Call `context-checkpoint` after each strategy completes
- Call `knowledge-compilation` after each strategy
</HARD-GATE>

## Guiding Principles

- **Correlation is not causation.** Every causal edge must have mechanistic justification, not just statistical association.
- **Confounders are everywhere.** Actively search for confounding variables that could explain observed relationships.
- **Interventions reveal truth.** The strongest evidence for causation comes from intervention studies.
- **Feedback loops are the norm.** Most real systems have circular causation. Document loops explicitly.
- **Confidence is calibrated.** Strong mechanism + strong evidence = high confidence. Weak either = low confidence.