Causal Modeling

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

Budget Table

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

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.