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Causal-Claim Labeling Template

Governance artifact — instantiates Correlation Structure Characterization

Stamps each correlation finding with the strongest causal claim its evidence can bear and the decisions it may license, so an association can't quietly graduate into a cause.

A number never travels alone — it travels with the sentence someone writes around it, and that sentence is where "X correlates with Y" turns into "X causes Y." Causal-Claim Labeling Template is the fill-in artifact attached to every reported correlation that forces its author to declare the claim type it supports — association only, predictive, conditional-independence-adjusted, or causal-under-named-assumptions — and the uses that claim permits. Its defining move is that it computes nothing and measures nothing: it caps interpretation. Where a partial-correlation probe removes a confounder arithmetically, this template governs the language and the downstream decisions, stamping a ceiling on what the finding may be used to assert so that ceiling rides along with the number wherever it goes.

Example

A hospital's population-health dashboard surfaces that patients with higher coffee intake show lower all-cause mortality. The temptation is a headline — "coffee protects." The analyst instead fills the template. Claim type: association only. Named threats: coffee drinkers in this sample skew younger and include fewer heavy smokers, both of which independently lower mortality. Strongest supportable statement: "in this cohort, coffee drinkers had lower mortality; this may reflect confounding, not benefit." Permitted use: hypothesis generation and cohort description — not a clinical recommendation and not an input to guideline changes. The finding is published with that label fixed to it, so a reader three teams away can't lift the coefficient and turn it into advice the evidence never earned.

How it works

The template's spine is a claim ladder — association → predictive → confound-adjusted → causal-with-design — where each higher rung requires specific, named evidence to occupy it. The finding is stamped at the highest rung its evidence actually licenses, and any phrasing above that rung is blocked before publication. Alongside the rung, the author records the scope (the population and conditions under which even the permitted claim holds) and the permitted decision uses, plus the assumptions that would have to be established to climb one rung higher. The output is not a verdict on the relationship but a disciplined boundary on how it may be spoken about and acted on.

Tuning parameters

  • Claim-ladder granularity — how many rungs the ladder distinguishes. More rungs draw finer lines but add labeling friction and can breed false precision about how "causal" a finding is.
  • Default rung — where an unlabeled finding sits until evidence lifts it. A conservative default (association-only) is safer but risks being ignored; a permissive default is convenient and dangerous.
  • Assumption-disclosure depth — whether the label names the confounders and conditions or just the rung. Naming them makes the ceiling defensible; omitting them makes the label a bare stamp.
  • Sign-off — self-labeled versus independent reviewer. Independent sign-off resists wishful upgrading; self-labeling is faster and weaker.
  • Enforcement point — advisory annotation versus a gate that blocks a stronger claim from shipping. The gate has teeth; the annotation relies on goodwill.

When it helps, and when it misleads

Its strength is that it attacks the archetype's signature failure directly: it stops a descriptive association from being narrated into an unsupported intervention, and because the label is attached to the finding it constrains readers who never saw the analysis. It makes the ceiling explicit rather than leaving each downstream reader to infer one.

Its failure modes are social, not statistical. A labeling ritual decays into box-ticking — a "causal" stamp applied without the design that would earn it — and a conservative label is easily bypassed by a reader who only reads the headline number. The classic misuse is running it backwards: applying the label after a decision to manufacture a causal story someone already wanted. The discipline that guards against this is to require the named evidence and assumptions for each rung and to route the sign-off through someone without a stake in the conclusion, weighing the association against structured criteria for causal plausibility rather than the strength of the coefficient alone.[1]

How it implements the components

Causal-Claim Labeling Template realizes the use-governance side of the archetype — the components that bound what an association may be claimed to mean:

  • correlation_use_contract — the completed label is the per-finding contract of permitted claims and uses.
  • causal_claim_guard — the claim ladder blocks any assertion above the rung the evidence licenses.
  • causal_claim_scope — it records the exact population and conditions under which even the permitted claim holds.
  • decision_use_translation — it states which decisions the finding may and may not inform.

It does NOT produce or size the association itself — correlation_or_dependence_profile and relation_strength_indicator come from the Nonlinear Dependence Screen and the Correlation Heatmap, and the confound-adjusted conditional_and_segment_structure from the Partial-Correlation or Residual Probe; nor does it establish that a survivor is beyond chance (multiplicity_control_record — Permutation Null and Multiplicity Check). It governs how the result is claimed, not how it is computed.

Notes

The template guards against causation, not chance: a finding can be labeled "association only" and still be spurious, or be beyond chance and still be non-causal. Pair it with the Permutation Null and Multiplicity Check, which polices a different failure mode — the two are complementary, not substitutes.

References

[1] The Bradford Hill viewpoints (strength, consistency, temporality, dose-response, plausibility, and others) are a standard checklist for weighing whether an observed association plausibly reflects causation. They are aids to judgement, not a proof procedure — which is exactly why the label names them as a rung's evidence, not as a rubber stamp. ↩