causal-modeling

name	causal-modeling
description	Campaign for building causal models — identify variables, map mechanisms, collect evidence, analyze interventions, validate models. Produces causal graphs in the wiki vault.
execution	campaign
used-by	knowledge-structuring

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

- Call `context-init` at campaign start - Call `context-checkpoint` after each strategy completes - Call `knowledge-compilation` after each strategy

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.