C.28 - CausalUse-CAL: Causal-Use Questions, Identification, and Realizability

Type: Calculus (C) Status: Stable Normativity: Normative unless explicitly marked informative

Plain-name. Causal-use calculus.

Intent. Help a practitioner decide what a causal-looking claim is supported to say, under which limits, and which narrower statement remains when the support is insufficient.

Primary EntityOfConcern. The causal-use question raised by a concrete claim: what would change under an intervention, what explains an observed difference, or what can be said about a counterfactual.

Scope. Use the domain’s causal models, intervention and outcome definitions, and scientific evidence to determine which statement is supported and under which limits.

C.28:0 - Use This When

Use C.28 when a result is offered as support for a causal effect, intervention, counterfactual comparison, causal fairness claim, causal policy, causal benchmark, or causal explanation. Common cues include:

• “method A improves the outcome”;
• “users who received X did better, so X works”;
• “this policy would have prevented the failure”;
• “the model shows what would have happened”;
• “this fairness metric proves the intervention is fair”; and
• “this benchmark shows that one causal method is better”.

The cue opens a question, not a verdict. Ask what claim is being supported and what use of the evidence depends on that support.

Not this pattern when. If the task only reports a measurement, temporal change or model output without causal reliance, continue with that direct task. Section :4.11 locates the relevant neighboring pattern when a return is needed.

Simulation at entry. “The simulator produced these traces” can finish as a model-output report. “These traces support what would happen under policy P” opens C.28: identify the model, assumptions, validation and the causal use they support.

C.28:0.1 - What Goes Wrong If Missed

• association becomes an intervention-effect claim;
• a changed metric becomes causal fairness;
• a simulated trace becomes realized counterfactual evidence;
• an estimated number is treated as proof that its estimand was identified;
• support for one population or environment is transported to another without an endpoint or assumption; or
• a support verdict is mistaken for permission to publish, deploy, or certify.

C.28:0.2 - What This Buys

The first result is a supported statement with its limits and the next useful step. Section :4.0 locates additional support components by the question each answers; open a specialist profile only when its result is needed.

C.28:0.3 - First-Minute Questions

1. What is the concrete claim, and what causal question must be answered to rely on it?
2. Is the requested statement about an observed association, an intervention, or a counterfactual?
3. What observations, experiments, model assumptions or derived results are available?
4. Which live threat could overturn the conclusion: for example, confounding, time order, missing comparison cases, interference, measurement error or transfer to another population?
5. What statement is supported under those conditions, and what further evidence or calculation would change it?

When the causal question requires relations that have not yet been constructed, use C.28.CM to turn the subject account and material alternatives into explicit causal models. Return with their premises, conditional consequences or a precise missing relation. A sufficient support verdict or an adequate existing model can finish the present question without that construction.

C.28:0.4 - First Output

Ordinary first result. Suppose the available comparison says that self-selected teams using method A completed more tasks than teams not using it, while task difficulty and prior team capability were not controlled. Report the observed association; the claim that A caused the improvement remains unsupported by that comparison. The next useful question is whether a design or existing evidence can distinguish the method’s effect from those rival explanations.

This sentence-level result can finish the task. When the triage must be reused, its local form is:

CausalUseTriageRecord:
  causalUseQuestionRef?: CausalUseQuestionRef
  causalUse: yes | no | unclear
  targetCausalityLadderRung?: CausalityLadderRung
  comparatorOrCounterfactualRef?
  availableSupportCues?
  liveThreats?
  supportedUse?
  unsupportedUse?
  nextCausalUseAction

supportedUse states the causal statement or evidential reliance supported under the named limits. unsupportedUse states the nearby stronger statement or reliance left unsupported by that evidence.

nextCausalUseAction =
  stopNoCausalUse |
  reportAssociationOnly |
  keepNonCausalSimulationUse |
  downgradeCausalWording |
  requestIdentificationOrBound |
  requestEstimate |
  requestCounterfactualSamplingRealizabilityCheck |
  requestPerformedSamplingEvidence |
  requestTransportCheck |
  requestEvidenceDesign |
  requestModelConstruction |

  sendFairnessUseToD5BiasAuditReport |
  sendParityUseToG9 |
  abstainDownstream

Triage may be the final result when it blocks the overclaim and names the narrower statement. Do not open a durable object merely because a causal word appears.

C.28:1 - Problem Frame

The practical question is “what does this evidence support us to say about this causal question, and what would overturn that conclusion?”

C.28:2 - Problem

Three kinds of collapse can produce causal overclaim:

1. Rung collapse: observation, intervention, and counterfactual comparison are treated as the same question.
2. Support collapse: data regime, identification, estimation, direct sampling, and simulation are treated as one alternative-valued “basis”.
3. Authority collapse: an evidential conclusion is treated as publication, choice, deployment, fairness, or assurance authority.

C.28 keeps those distinctions visible while allowing a cheap stop.

C.28:3 - Forces

C.28:4 - Solution

Use the smallest result that answers the current question:

1. triage the claim;
2. stabilize the question in a small card when it must be reused;
3. run the common threat screen;
4. add only the specialist result needed now; and
5. issue a small CausalUseSupportResult when another pattern must consume the conclusion.

C.28:4.0 - Public contract and support components

When a question or result must be referenced, recover its content and use the corresponding contract:

• CausalUseQuestionRef identifies the exact question content, normally a C.2.1 episteme.
• CausalEstimandRef identifies the mathematical target or the episteme that describes it under its direct pattern.
• PotentialOutcomeContrastRef identifies the exact contrast or its description.
• CausalUseSupportResultRef identifies one C.2.1 result episteme defined below.

Support is composable. A real result may use several components:

CausalSupportComponentRefs:
  evidencePathRefs?
  empiricalDataRegimeRefs?
  identificationResultRef?
  estimateResultRef?
  counterfactualSamplingRealizabilityResultRef?
  simulationResultRef?
  targetTrialMappingResultRef?
  offPolicyCausalEvaluationResultRef?
  causalVariableRepresentationRecordRef?
  transportabilityResultRef?

These fields answer different questions. Do not compress them into one exclusive value. Each optional specialist ref identifies the exact result or record actually used. Keep a component’s own assumptions, uncertainty or sensitivity, supported and unsupported uses, and reopen information with that component when its defining contract requires them; do not copy fields merely to complete a standard list. The common CausalUseSupportResult still states its own supportedUse, unsupportedUse, limits, optional evidence window, and reopenCondition. Naming a specialist subject in prose does not make its result available to a consumer.

CausalEmpiricalDataRegime is a local classification used only when it helps distinguish evidence:

CausalEmpiricalDataRegime =
  observationalOrNaturalBehaviorData |
  randomizedInterventionData |
  governedInterventionData |
  realizedCounterfactualSamplingData

realizedCounterfactualSamplingData is used only when an A.10 evidence path cites dated sampling Work and the resulting sample or data. A realizability result or WorkPlan alone establishes no empirical regime. Model output is recorded separately through simulationResultRef, not as an empirical regime.

C.28:4.1 - Causality-Ladder Rung

CausalityLadderRung =
  observationalAssociationRung |
  interventionalActionRung |
  counterfactualComparisonRung

• observational: passive observation, natural behaviour, or association;
• interventional: action setting, experiment, policy change, or action effect;
• counterfactual: counter-to-fact, potential-outcome, or unit-history-conditioned comparison.

Lower-rung data may contribute to a higher-rung result only through a replayable identification, bound, or other specialist result. The rung label itself supplies no support.

C.28:4.1a - Causal-Use Claim Kind

CausalUseClaimKind =
  causalEffectClaim |
  counterfactualComparisonClaim |
  causalFairnessClaim |
  causalPolicyClaim |
  causalBenchmarkParityClaim |
  causalEvidenceSupportClaim |
  causalAssuranceSupportClaim

Choose the kind by the claim being supported, not by the tool or source. Simulation for a causal claim uses the appropriate claim kind plus simulationResultRef; it does not need a simulation-only claim kind.

C.28:4.2 - Question cards and support result

Use a local card when the question must survive beyond the current sentence:

LocalCausalUseQuestionCard:
  causalUseQuestionRef: CausalUseQuestionRef
  targetCausalityLadderRung: CausalityLadderRung
  causalUseClaimKind?
  comparatorOrCounterfactualRef?
  causalEstimandRef?
  supportedUse
  unsupportedUse
  nextCausalUseAction

Use a durable card only for a reusable or consequential claim:

DurableCausalUseQuestionCard:
  causalUseQuestionRef: CausalUseQuestionRef
  targetCausalityLadderRung: CausalityLadderRung
  causalUseClaimKind
  comparatorOrCounterfactualRef?
  causalEstimandRef: CausalEstimandRef
  potentialOutcomeContrastRef?: PotentialOutcomeContrastRef
  interventionOrAssignmentWindowRef?
  followUpWindowRef?
  outcomeMeasureRef?
  causalAssumptionRefs
  rivalCauseRefs?
  causalSupportComponentRefs
  commonThreatScreenRef?
  supportedUse
  unsupportedUse
  stopOrReopenCondition

When another pattern needs a stable conclusion, issue this small C.2.1 result episteme:

CausalUseSupportResult:
  causalUseQuestionRef: CausalUseQuestionRef
  causalUseClaimKind: CausalUseClaimKind
  targetCausalityLadderRung: CausalityLadderRung
  causalEstimandRef?: CausalEstimandRef
  causalSupportComponentRefs: CausalSupportComponentRefs
  commonThreatScreenRef?
  verdict: supported | bounded | unsupported | undecided
  supportedUse
  unsupportedUse
  limits
  evidenceWindowRef?
  reopenCondition

Its identity and reference follow C.2.1. A receiving decision consumes it under :4.9.

C.28:4.3 - Common causal-validity screen

Run only the questions relevant to the current claim. A live threat either points to an existing specialist field/result or lowers the support result; it does not trigger a mandatory dossier.

CommonCausalThreatScreen:
  causalUseQuestionRef
  interventionWellDefinedOrConsistency?: clear | liveThreat | notApplicable
  temporalOrdering?: clear | liveThreat | notApplicable
  exchangeabilityOrConfounding?: clear | liveThreat | notApplicable
  positivityOrOverlap?: clear | liveThreat | notApplicable
  interferenceOrSpillover?: clear | liveThreat | notApplicable
  selectionCensoringOrMissingness?: clear | liveThreat | notApplicable
  measurementErrorOrConstructShift?: clear | liveThreat | notApplicable
  transportToTarget?: clear | liveThreat | notApplicable
  routedThreatRefs?
  resultingSupportBoundary

Ordinary effect case. A randomized treatment study records interventionWellDefinedOrConsistency=clear, temporalOrdering=clear, positivityOrOverlap=clear, interferenceOrSpillover=notApplicable, selectionCensoringOrMissingness=clear, and measurementErrorOrConstructShift=clear for its declared target and window. The screen points to the trial and estimate results; it does not repeat them.

Countercase. An observational cohort has the right rung label and a plausible estimand, but records exchangeabilityOrConfounding=liveThreat and positivityOrOverlap=liveThreat because severity is unmeasured and one treatment region has no comparator. The claimed effect remains unsupported until a suitable design or new evidence closes those threats; a suitable bound can instead support a correspondingly bounded claim. “Observational data” was classified correctly; that label does not establish validity.

C.28:4.4 - Identification result

Identification answers whether the estimand can be expressed or bounded from the available data and assumptions. The bounds for nonidentified counterfactual queries in Raghavan and Bareinboim, 2026, §5 belong to this identification problem. The conclusion must be replayable:

CausalIdentificationResult:
  causalUseQuestionRef: CausalUseQuestionRef
  causalEstimandRef: CausalEstimandRef
  availableDataRegimeRefs
  causalAssumptionRefs
  modelOrDiagramRefs?
  calculusOrDerivationMethodRef?
  status: identified | bounded | nonidentified | unclear
  identifyingExpressionOrDerivationRef?   # required when identified
  boundResultRef?                         # required when bounded
  obstructionOrFailureWitnessRef?         # required when nonidentified
  falsificationOrNegativeControlRef?
  sensitivityAnalysisRef?
  supportedUse
  unsupportedUse

An identified label without an identifying expression or derivation is incomplete. A bounded result cites the bound. A nonidentified result exposes the obstruction or failure witness. Identification is neither a numerical estimate nor direct physical sampling.

Replayable identified case. For treatment_effect_in_population_P, AdjustmentSet_Z is justified as blocking the relevant back-door paths. backdoor_adjustment_derivation_7 states the identifying expression in ordinary terms: compare treated and untreated outcomes within each Z group, then average those differences using the target population’s Z distribution. The result cites the data regime, assumptions, expression, and the confounding or overlap change that would reopen it.

Replayable nonidentified case. In a treatment cohort, unmeasured severity affects both treatment and outcome, and no valid adjustment set, instrument, proxy, or useful bound is available. unmeasured_severity_obstruction_3 is the failure witness. The result is nonidentified; reporting an adjusted number does not change that status.

For a supplied causal model, C.28.MR derives the consequence by replacing the selected mechanism, retaining the other mechanisms and input law, and solving the relations needed by the query. The following case gives its small observation/intervention entry.

Replayable sensor case: observation and intervention. Let H denote binary high load and S a binary alarm. Stipulate P(H=1)=0.5, P(S=1|H=1)=0.9 and P(S=1|H=0)=0.1. Bayes’ rule gives P(H=1|S=1)=0.9 and P(H=1|S=0)=0.1. These are observational questions about the stated joint distribution.

For the query P(H=1|do(S=0)), additionally specify a structural model: H is determined by an exogenous random input; S is a noisy measurement of H with separate independent noise; S has no influence on H. Replacing the S mechanism by the constant zero preserves the H mechanism and its input distribution, giving P(H=1|do(S=0))=0.5. This derivation is an identified model-based result under those assumptions. The corrected Pearl, Glymour and Jewell primer, p.55 explains the mechanism-replacement operation.

If the alarm controls cooling, specify the intervention time and the subsequent load mechanism before answering a later-load question. F.0.2:5.5 uses this distinction when comparing source theories for an explanation.

C.28:4.5 - Counterfactual sampling realizability

Use this result to answer whether a declared target distribution can be sampled under current constraints. Raghavan and Bareinboim, 2025, Definition 3.4 and Theorem 3.5 give a construction-or-failure decision for their graph and available-action setting. The result is prospective: it does not say that sampling was planned, performed, or yielded data.

CounterfactualSamplingRealizabilityResult:
  causalUseQuestionRef: CausalUseQuestionRef
  targetCounterfactualDistributionRef
  targetCausalityLadderRung: counterfactualComparisonRung
  modelOrDiagramRefs?
  sameUnitConflictCheck
  ancestorRegimeConflictCheck
  physicalConstraintRefs
  ethicalConstraintRefs
  operationalConstraintRefs
  unitHistoryAvailabilityRef?
  decisionMethodRef
  decisionDerivationRef?
  positiveSamplingConstructionRef?  # required when realizable

  obstructionOrFailureWitnessRef?   # required when nonrealizable
  status: realizable | nonrealizable | unclear
  supportedUse
  unsupportedUse

A realizable result cites the sampling construction that the decision Method accepts for the exact target and constraints. A nonrealizable result exposes the obstruction or failure witness. unclear names what remains unresolved. Bounds on a counterfactual probability belong to the identification result at :4.4; they constrain what can be inferred about that probability, while this result asks how draws from the target distribution can be obtained. “Realized counterfactual sampling” never means observing incompatible outcomes for one unit in one realized world.

If the team plans to draw samples, use a separate A.15.2 WorkPlan. If sampling occurs, recover every precise performer’s A.13 core and independently admit the dated Work under A.15.1. Add F.6 only when the sampling claim also needs precise assignment-bound attribution. If the samples are used as evidence, cite the resulting data through an A.10 evidence path. Actual sampling support requires both the dated Work and resulting data or evidence ref; neither realizable nor a WorkPlan can stand in for them. Identification from those data, when claimed, is another CausalIdentificationResult.

C.28:4.6 - Applied profiles

Target trial. The TARGET Statement, 2025 supplies reporting guidance for an observational study explicitly emulating a target trial. Use it for the protocol-to-data account below, with causal validity checked separately.

TargetTrialProtocolRecord:
  causalUseQuestionRef: CausalUseQuestionRef
  targetPopulationRef
  eligibilityCriteriaRef
  treatmentStrategyRefs
  assignmentProcedureRef?
  timeZeroRef
  followUpWindowRef
  outcomeMeasureRef
  potentialOutcomeContrastRef?: PotentialOutcomeContrastRef
  causalEstimandRef: CausalEstimandRef
  analysisPlanRef

An observational emulation keeps the protocol and its mapping to available data as separate results:

TargetTrialMappingResult:
  causalUseQuestionRef: CausalUseQuestionRef
  targetTrialProtocolRef
  observationalDataSourceRef
  eligibilityCriteriaMappingRef
  treatmentStrategyMappingRefs
  assignmentAndTimeZeroMappingRef
  followUpWindowMappingRef
  outcomeMeasureMappingRef
  identifyingAssumptionRefs
  protocolToDataGapAccountRef
  residualConfoundingAssessmentRef
  sensitivityMappingRefs
  supportedUse
  unsupportedUse
  reopenCondition

Every mapping field identifies the actual mapping. protocolToDataGapAccountRef points to one account that lists the observed gaps or explicitly states that none was found within the declared source and window. The residual-confounding and sensitivity fields remain present even when their bounded result is favourable. Reporting completeness is not a risk-of-bias, identification, or estimate verdict.

Filled target-trial mapping. HypertensionEmulationMap-2025 maps HypertensionTargetTrial-1 to ClinicRecords-2022-2024: age and diagnosis fields implement eligibility; prescription records distinguish the two treatment strategies; the prescription date supplies assignment and time zero; encounter records map the twelve-month follow-up; and the recorded systolic-pressure field maps the outcome. GapRecord-17 states that adherence after prescription is not observed, ResidualConfoundingAssessment-17 retains unmeasured severity as a live threat, and SensitivityMap-17 points to the negative-control and quantitative-bias analyses. The result supports construction and review of this emulation. It does not by itself establish identification, low bias, or a transportable effect; new severity or adherence data reopens it.

Estimation.

CausalEstimateResult:
  causalEstimandRef: CausalEstimandRef
  identificationResultRef?: CausalIdentificationResultRef
  designBasedIdentificationResultRef?
  dataRef
  estimatorMethodRef
  diagnosticRefs?
  uncertaintyResultRef
  sensitivityAnalysisRef?
  estimationConsistencyResultRef?  # when consistency is a live support condition
  methodSpecificDetailRefs?        # only for the selected estimator family
  supportedUse
  unsupportedUse

At least one identification or explicit design-based basis is required before the estimate supports a causal use. Orthogonal scores, nuisance models, and cross-fitting belong in methodSpecificDetailRefs only when a DML Method is selected. estimationConsistencyResultRef points to the consistency result defined by the selected estimation Method or its direct evaluation pattern.

Counterfactual fairness. Before D.5 uses a counterfactual-fairness support result, its C.28 components cite the identification result and the extra assumptions needed to connect the available data to that counterfactual question. When the fairness conclusion depends on an estimate, they also cite the estimate and its estimationConsistencyResultRef. Without those conditions, return bounded or unsupported; more data, even an unlimited amount of the same data, does not repair missing counterfactual identification or an inconsistent estimator. Associative or interventional fairness claims use their own rung and do not inherit this stronger branch by label.

Non-DML estimate. A randomized trial cites random_assignment_identification_4, trial_data_8, DifferenceInMeansMethod_2, standard_error_result_5, and its attrition sensitivity check. It needs no orthogonal-score, nuisance-model, or cross-fitting fields. The estimate supports only the declared population, outcome, assignment, and follow-up window.

Transport. For the first-moment population-measure problem under covariate shift, use Boughdiri, Berenfeld, Josse and Scornet, A Unified Framework for the Transportability of Population-Level Causal Measures, 2025, §§2–4 with its internal trial-validity, overlap and selected exchangeability conditions. Other endpoint changes need their own identifying result.

CausalTransportabilityResult:
  causalUseQuestionRef: CausalUseQuestionRef
  sourcePopulationRef?
  targetPopulationRef?
  sourceDomainRef?
  targetDomainRef?
  sourceEnvironmentRef?
  targetEnvironmentRef?
  sourceDataGeneratingRegimeRef?
  targetDataGeneratingRegimeRef?
  selectionAssumptionRefs?
  domainShiftAssumptionRefs?
  sourceWindowRef?
  targetWindowRef?
  overlapEvidenceRef?
  transportComparatorOrFormulaRef
  semanticBridgeRef?          # only when interpretation differs
  supportedUse
  unsupportedUse
  unresolvedAssumptionRefs?

Identify every endpoint dimension that differs in the current claim. Population, domain, environment, data-generating regime, and semantic scheme answer different questions. A shared label proves nothing; a semantic Bridge is added only when its F.9 relation independently obtains.

Off-policy evaluation.

OffPolicyCausalEvaluationResult:
  causalUseQuestionRef: CausalUseQuestionRef
  evaluationPolicyRef
  behaviorPolicyRef
  sequentialHorizonRef?
  unitHistoryConditioningRef?
  confoundingAssumptionRefs?
  overlapOrSupportCheckRef
  policyTransportabilityResultRef?
  estimatorRef?
  uncertaintyResultRef?
  supportedUse
  unsupportedUse
  reopenCondition

Causal representation. Use this record only when variables are learned, selected, or abstracted rather than supplied by the domain:

CausalVariableRepresentationRecord:
  causalUseQuestionRef: CausalUseQuestionRef
  sourceRepresentationRef
  selectionOrAbstractionMethodRef
  representationAssumptionRefs
  interventionValidityResultRef
  invarianceResultRefs?
  abstractionFidelityResultRef?
  counterfactualQueryPreservationResultRef?
  uncertaintyResultRef?
  shiftLimitRefs?
  supportedUse
  unsupportedUse
  reopenCondition

The record states which interventions and queries the learned or abstracted variables preserve, not that they are causal variables for every query or domain.

Filled representation case. WardStateRepresentation-4 derives three state variables from monitor traces through WardStateAbstractionMethod-2. Its intervention-validity result covers dosage interventions, its invariance result covers the two hospitals in the training and hold-out comparison, and its query-preservation result passes the declared one-step counterfactual query but fails the long-horizon query. The record therefore supports the one-step policy comparison only; a new hospital, sensor scheme, intervention family, or long-horizon claim reopens it.

C.28:4.7 - Graph and calculus names

Use C.28.CM when the causal relations, material alternatives or observing process still need to be modeled. It supplies a model with explicit premises and a useful consequence or unresolved distinction. Use C.28.MR for a required intervention derivation in a supplied model. These contributions return to the support question here; constructing a graph does not establish its empirical adequacy.

Use specialist names only when the result depends on them. For a counterfactual graphical-model derivation, use the conditions and calculus in Correa and Bareinboim, 2025 and cite the actual derivation used:

CausalGraphRepresentationKind =
  causalDirectedAcyclicGraphRepresentation |
  acyclicDirectedMixedGraphRepresentation |
  singleWorldInterventionGraphRepresentation |
  structuralCausalModelTwinNetworkRepresentation |
  ancestralMultiWorldNetworkRepresentation |
  counterfactualGraphicalModelRepresentation

GraphSeparationCriterionKind =
  dSeparationCriterion |
  mSeparationCriterion |
  singleWorldInterventionGraphSeparationCriterion |
  ancestralMultiWorldNetworkSeparationCriterion |
  counterfactualGraphSeparationCriterion

CausalInferenceCalculusKind =
  doCalculus |
  ctfCalculus |
  potentialOutcomeCalculus |
  gFormulaCalculus

These values classify the formal support form. Concrete refs point to the model, diagram, derivation, assumptions, or proof. A graph-class label is not a proof and does not replace the plain statement of what was identified or bounded.

C.28:4.8 - Causal evidence design and Work

Use CausalUseEvidenceDesignRecord when additional evidence could change the support boundary:

CausalUseEvidenceDesignRecord:
  causalUseQuestionRef: CausalUseQuestionRef
  targetCausalityLadderRung
  causalEstimandRef?
  interventionOrProtocolRef?
  plannedDataRegimeRefs?
  identificationQuestionRef?
  estimationQuestionRef?
  samplingRealizabilityQuestionRef?
  transportQuestionRef?
  targetTrialMappingResultRef?
  offPolicyCausalEvaluationResultRef?
  causalVariableRepresentationRecordRef?
  causalEvidenceMethodDescriptionRefs?
  causalEvidenceWorkPlanRef?
  realizedCausalEvidenceWorkRefs?
  workAttributionResultRefs?
  evidencePathRefs?
  modelAssumptionRefs?
  simulationValidationRef?
  decisionThresholdAffected?: yes | no | unclear
  evidenceValueOrProbeWorthinessRef?
  costOrRiskRef?
  supportedUseIfSuccessful
  unsupportedUseWithoutFurtherEvidence

The three optional specialist refs are included only when an existing target-trial mapping, off-policy evaluation, or causal-variable representation result shows what additional evidence could change the support boundary. Before execution, cite a MethodDescription or WorkPlan only when used. After execution, cite every precise performer’s A.13 core and the independent A.15.1 Work admission; cite F.6 only when precise assignment-bound attribution is also current. If performed counterfactual sampling is used as evidence, also cite the resulting sample or data through evidencePathRefs; Work without output data and data without its Work and provenance path each remain incomplete for that claim. Do not copy performer-kind, assignment, or occurrence mechanics into this record unless one of those facts changes causal validity, safety, authorization, or supported use.

Additional evidence is worth planning only when it can change a material causal statement or downstream decision enough to justify cost, risk, and delay, or when safety or release rules independently require it.

C.28:4.9 - Support is not authority

CausalUseSupportResult.verdict has four values:

• supported: the named causal statement or evidential reliance is supported under the stated limits;
• bounded: only the narrower statement or reliance is supported;
• unsupported: the claimed causal statement or reliance is not supported;
• undecided: the available work does not establish a causal conclusion.

When another pattern uses the result for a decision, it checks whether the named support and limits answer its question and applies its own remaining decision conditions. For undecided, it chooses whether to seek evidence, abstain or use an available non-causal result. “Report association only” limits the evidential claim; any decision to publish, choose or deploy remains with the receiving pattern.

C.28:4.10 - Causal action policy class

Use CausalActionPolicyClass only when the action-selection regime changes the causal question. Identify the decision variables, horizon and natural mechanism; say whether the question compares one specified rule or an admissible family. Keep that rule or family with the question. When both behavior and evaluation policies occur, identify which one the field summarizes.

The mechanism distinctions follow Maiti and Bareinboim, Sequential Causal Games, Definitions 2.3, 2.5, 2.7–2.8. Let X° be the natural action and Z the allowed pre-action information. Order actions causally; Z excludes descendants of the current or later action variables.

For this interface, when a concrete rule is represented in the wider counterfactual family, record its simpler natural or interventional case when that reduction holds throughout the stated scope. Keep mixedPolicy when the specified combination matters. If selection itself requires the current natural proposal, expose that counterfactual dependence. For a family comparison, retain the declared available family and any restrictions. Thus the scalar is an informative summary of the supplied rule or family, not a disjoint ontology of policies.

Authored three-rule replay. For binary X°, compare:

The last evaluation needs the natural proposal and its relation to outcomes; a fixed-action evaluation does not answer it. A mixed example chooses with equal probability, using an independent coin, between following the natural mechanism and replacing its action by zero. Retain both component choices and their probabilities.

unknown records unresolved classification, not another member. Omit the field when it changes no support, comparison or receiving decision.

C.28:4.11 - Neighbor selection

C.28:4.13 - Cheap downgrade library

C.28:4.14 - Payoff check

Keep a causal-use record only when it changes the supported causal statement, blocks a concrete overclaim, changes evidence work, or supplies a real basis to a downstream decision. Remove fields that do none of those things. Prefer triage when it preserves the same boundary.

C.28:4.15 - Publication-unit boundary

When only wording inside one publication unit is unclear, use the publication and wording patterns. Open C.28 only when the wording is relied on causally.

C.28:4.16 - Causal-laundering cases

C.28:5 - Archetypal Grounding

System. A product team observes better outcomes among recipients of X. Triage returns association support. If the team needs an effect claim, it opens identification or evidence-design work; the deployment decision still needs its own downstream basis.

Fairness. A report claims counterfactual fairness after a policy change. C.28 identifies the rung and estimand, exposes the additional counterfactual-identifiability assumptions, and cites an estimate with its consistency result when the audit relies on that estimate. Missing identification or consistency lowers the support result even with more of the same data. D.5 carries the BiasAuditReport@Context and makes the audit conclusion.

Policy. Logged behaviour data are used to evaluate a new policy. The result names both policies, horizon, confounding and overlap checks, transport endpoints when changed, estimate and uncertainty, supported regime, and unsupported unqualified optimality. C.11 or another policy pattern makes the choice.

Causal RL. An online learner combines logged behaviour, interventions, and a counterfactual-data source. The sampling-realizability result explains whether that source can be produced; dated Work and the resulting data path show whether it was produced; a separate identification or estimate result says what follows from it. Replay reward does not become an optimal-action claim.

Evidence Work. A lab’s CounterfactualSamplingRealizabilityResult cites its decision Method and positive construction. That result supports planning but claims no sample. The later WorkPlan remains prospective. After sampling, the lab cites independently admitted dated Work and the resulting data in an A.10 evidence path before using realizedCounterfactualSamplingData; it adds precise assignment-bound attribution only when the receiving support claim uses it. Identification from those data remains a separate result.

Simulation. A simulator supports rehearsal and sensitivity analysis under named assumptions and validation. The support result blocks realized-sample and intervention-effect wording. A pure simulator-output report exits C.28 earlier.

Transport. The population is unchanged but the care environment and measurement mechanism differ. The transport result names source and target environments and data-generating regimes, then states the assumptions and formula. A population ref alone would miss the shift.

Benchmark. G.9 compares an observational predictor, intervention optimizer, and counterfactual policy only after it checks rung, estimand, support components, window, and endpoints. The admissible result may be a selected set or abstention rather than a scalar winner.

C.28:6 - Bias-Annotation

Watch for causal prestige, simulation laundering, metric proxy substitution, graph sufficiency, feasibility-as-performance, data-without-Work, support-label substitution, and benchmark scalarization. Recover the question, support components, live threats, supported statement, unsupported statement, and reopen condition in the shortest form that remains replayable.

C.28:7 - Conformance Checklist

1. The concrete claim and exact causal-use question remain identifiable from entry to the supported statement and its limits.
2. Every reference required by the current use resolves to the exact question, target or result defined at :4.0; a sentence-level triage can finish without such references.
3. Data regime, identification, estimate, sampling realizability, performed sampling evidence, simulation, and transport remain distinct and may be combined.
4. A downstream decision uses the causal-support result within its stated limits and checks its remaining conditions under the receiving pattern, as required by :4.9.
5. An identified result cites an expression or derivation; a bounded result cites a bound; a nonidentified result cites an obstruction or witness.
6. A causal estimate cites an identification or explicit design-based result. Method-family details appear only when that Method is selected.
7. The common threat screen routes every live ordinary threat or lowers the result; it is not a mandatory dossier.
8. Non-causal simulator reporting and simulation-supported causal use take different routes at first entry.
9. A sampling-realizability result cites its decision Method, any derivation used, and the sampling construction or obstruction required by its status; unclear names the unresolved question. Counterfactual-quantity bounds remain in the separate identification result. A prospective result claims no Work or data.
10. Performed counterfactual-sampling support cites independently admitted dated Work and resulting data or evidence; it cites exact assignment-bound attribution only when the receiving support claim uses it. A WorkPlan or realizable label cannot satisfy this branch.
11. Before execution, evidence design cites a MethodDescription or WorkPlan only when used. When it cites performed Work, it identifies each precise performer under A.13 and the dated occurrence independently under A.15.1. Performed counterfactual sampling used as evidence also cites the resulting data through A.10. F.6 is required only when that account uses exact assignment-bound attribution.
12. Transport identifies every changed population/domain/environment/data-generating-regime endpoint separately from semantic schemes.
13. A counterfactual-fairness escalation exposes its additional identification assumptions and, when an estimate is used, estimation consistency before D.5 consumes it.
14. CausalActionPolicyClass identifies the specified rule or available family through :4.10’s mechanism and information conditions; reductions, combinations and unresolved classifications stay explicit. Consumers use the same meaning and omit an unused field.
15. Every specialist field changes support, a downstream decision basis, evidence work, or a reopen condition.
16. A target-trial mapping result identifies the observational source and every protocol-to-data mapping, gap, residual-confounding assessment, and sensitivity mapping needed for its bounded use.
17. Every retained specialist result that can independently change support can enter CausalSupportComponentRefs; when it shapes further evidence, the evidence-design record can cite the same result without copying it.
18. The whole pattern remains understandable to a practitioner without requiring the formal graph vocabulary on the ordinary path.

C.28:8 - Common Anti-Patterns and How to Avoid Them

C.28:9 - Consequences

The pattern makes unsupported causal claims easier to lower while keeping ordinary triage cheap. Consequential claims become replayable across question, support components, threats, limits, and source window. The cost is additional specialist work only where a stronger causal statement or downstream decision genuinely depends on it.

C.28:10 - Rationale

Temporal change, a higher metric, a convincing graph, or a plausible simulator can all be useful without supporting a causal effect. Conversely, observational data can support a causal estimate when an explicit identification result closes the inferential gap. C.28 therefore separates the question from the components that support it and separates that evidential conclusion from downstream authority.

C.28:11 - SoTA-Echoing

Which question does the available model answer? In :4.4, the same observed alarm distribution gives P(H=1 | S=0)=0.1, while replacing the alarm mechanism gives P(H=1 | do(S=0))=0.5 under the stipulated no-feedback model. Reusing the observational answer is cheaper but answers the wrong question when the user proposes to set the output. Mechanism replacement, as explained in Pearl, Glymour and Jewell’s corrected primer, p.55, supplies the operation used in the second derivation.

This comparison selects :0.3’s question distinction and :4.4’s requirement for an identifying expression or derivation. The added cost is stating the causal mechanism and assumptions needed for the requested intervention, instead of relying on the joint distribution alone. If the requested statement is observational, its existing answer suffices. If the alarm initiates cooling, the later-load question requires the changed mechanism and time order; reopen the earlier intervention result.

What supports a counterfactual-fairness conclusion? The analysis by Ma, Melnychuk, Frauen and Feuerriegel, 2026 identifies two failures in counterfactual-fairness baselines: missing counterfactual-identifiability assumptions and inconsistent counterfactual estimation. Their analysis uses identification up to a measure-preserving indeterminacy and a compatible consistency condition. More of the same data does not generally supply either missing condition.

For a receiving fairness audit, an improved metric or a large dataset therefore leaves a different question from the counterfactual guarantee. Section :4.6 selects a separate identification result and, when the conclusion uses an estimate, its Method’s consistency result before D.5 consumes the support. The additional work is justified by that stronger question; an associative disparity report can finish at its own rung. The source supplies this failure analysis and a method-specific remedy, so the consumed guarantee retains its assumptions and scope. Reopen the support when those assumptions, the estimator or the fairness question changes.

How much interface is useful? A domain analyst’s ordinary causal report can already state a question, assumptions, result and limitations. For the self-selected-team comparison in :0.4, that short report is sufficient: C.28’s thin path returns the association and the live confounding question. Requiring the complete specialist profiles would add target, model and result declarations unused by that conclusion.

When several receiving uses need the same conclusion, the alternative is repeatedly extracting its question and limits from a larger report. Sections :4.0–:4.2 instead provide references to the independently used results and one common support conclusion. This costs explicit identification of those results and their conditions. It can avoid copying an identification derivation, estimate or sampling construction into each receiver. Use that structure when the receiving work needs it; reuse an existing result directly when it already supplies the required information.

These are bounded selections for the illustrated questions. They preserve cheap prose, expose a mathematical difference when it changes the answer, and require stronger support for a stronger fairness claim. Reopen the selected form if it hides a live causal distinction or requires information that the receiving use does not consume.

C.28:12 - Relations

• C.28.CM constructs and challenges causal models when the mechanism relations or material alternatives are still missing; the returned model retains its assumptions and evidential limits.

• C.28.MR derives an intervention consequence within a supplied causal model, with the replacement, retained conditions and solution needed by that query.

• C.16 keeps measurements and scales; C.27 keeps temporal-claim adequacy.

• A.10 keeps evidence paths and provenance and may cite C.28 support components and result.

• A.2.4 classifies how an episteme is used; it cannot promote simulation output or association into stronger causal evidence.

• A.15 keeps Method, plan, Work, and attribution for interventions, target trials, and sampling.

• B.3 may cite a C.28 result as one basis for a separate bounded assurance result.

• C.11, C.19, and C.24 keep choice, pool treatment, and call planning and consume only the needed causal refs.

• D.5 keeps bias/fairness audit and uses BiasAuditReport@Context when a causal fairness question is consequential or reusable.

• G.5 keeps method dispatch; G.9 keeps parity and benchmark conclusions; G.11 keeps refresh planning.

• C.26 is used only for a residual quantum-like modelling issue after ordinary causal explanations are tried.

C.28:12.1 - C.29 mathematical-lens relation

C.29 may describe a mapping as abstraction-like, quotient-like, coarse-graining-like, simulation-like, or macro-model-like. It does not decide causal support. When intervention, policy, counterfactual, causal explanation, or causal decision use is current, apply C.28; otherwise record no causal-use claim or the exact blocker.

C.28:End