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

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.

Available Strategies

Optional, no fixed order; the final leaf is always a sop.

Strategy	When to use
evidence-collection	Gather evidence for causal claims
intervention-analysis	Analyze interventions and manipulations on the causal system
knowledge-structuring-variable-identification	Identify key variables in the causal system
mechanism-mapping	Map causal mechanisms between variables
model-validation	Validate causal model consistency

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

Tactic	When to use
counterfactual-reasoning	Tactic for reasoning about what would happen if variables were different — supports causal identification and intervention analysis.
evidence-weighing	Tactic for assessing the strength and relevance of evidence for causal claims — distinguishes correlation from causation.
feedback-loop-detection	Tactic for identifying circular causation — detect feedback loops, classify as reinforcing or balancing, document loop structure.
knowledge-compilation	Tactic for compiling research findings into vault pages — orchestrates page creation, updates, edge linking, and index maintenance. Minimum yield ≥3 page operations per invocation.

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
context-checkpoint	Append research process and results to the current Phase's context file. Covers both process and results with genuine substance. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase.
context-init	Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed.