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version: "1.0.1" 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 dependencies: strategies:

evidence-collection
intervention-analysis
knowledge-structuring-variable-identification
mechanism-mapping
model-validation

tactics:

counterfactual-reasoning
evidence-weighing
feedback-loop-detection
knowledge-compilation

sops:

context-checkpoint
context-init

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)

variable-identification — identify key variables in the causal system
mechanism-mapping — map causal mechanisms between variables
evidence-collection — gather evidence supporting/refuting causal claims
intervention-analysis — analyze what happens when variables are manipulated
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.

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.

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