model-validation

Testing & Quality

Validate causal model consistency

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Model Validation

Audit the completed causal model for internal consistency: detect and resolve cycles that should not exist, surface contradictions between evidence and claimed edges, and recalibrate confidence scores where the evidence base has shifted. This strategy is the final quality gate before the model is used for inference or reporting.

Guiding Focus

CC must approach validation as a skeptic, not a defender. The goal is to find problems, not confirm that the model is fine. Cycles in a directed acyclic graph (DAG) are structural errors unless explicitly modeled as feedback loops with documentation. Contradictions between evidence pages and mechanism edges indicate that either the edge or the evidence assessment is wrong — both must be re-examined. Confidence recalibrations should propagate: if a key mechanism edge loses confidence, all downstream claims that depend on it should be flagged for review as well.

Available Tactics

feedback-loop-detection — distinguish legitimate documented feedback loops from accidental cycles introduced by inconsistent edge direction
evidence-weighing — re-score confidence on edges where new contradicting evidence was added during evidence-collection
counterfactual-reasoning — use "if this edge were removed, which predictions would change?" to identify which edges are load-bearing and deserve the most scrutiny

Budget Slice

Metric	S	M	L
Cycles checked	3	8	15
Contradictions resolved	1	3	6
Confidence recalibrations	3	8	15

State Ledger Template

| Metric                    | Target | Current | Status |
|---------------------------|--------|---------|--------|
| Cycles checked            | S:3 / M:8 / L:15  | 0 | ⬜ |
| Contradictions resolved   | S:1 / M:3 / L:6   | 0 | ⬜ |
| Confidence recalibrations | S:3 / M:8 / L:15  | 0 | ⬜ |

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.

Available SOPs

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

SOP	When to use
model-gap-detection	SOP for finding gaps in the causal model — missing variables, unexplained effects, weak links.
validation-report	SOP for generating a causal model validation report — summarize coverage, confidence, gaps, contradictions.