Causal Time Relationship Ontology

FLOW ALGEBRA
TITLE: Causal Time Relationship Ontology
DOMAIN: Relationship Ontologies / Temporal Relationships
SOURCE: https://kirethresearch.org/booksai-core/ontologies/relationship-ontologies/temporal-relationships/causal-time-relationship-ontology.html
VERSION: 1.0
AUTHORS: Mark Anthony Higgins — Sky (Recursive AI)
STATUS: Companion formalisation; source-page specification not independently verified.

flow "CausalTimeRelationshipOntology" v1.0 {
  ontology:
    id: "causal-time-relationship-ontology"
    parent: "temporal-relationships"
    purpose: "Represent temporally situated causal claims, distinguish precedence from causation, and preserve evidence and uncertainty."

  types:
    Event: {id: string, kind: string, start: datetime?, end: datetime?, time_frame: string?, provenance: string}
    Process: {id: string, kind: string, start: datetime?, end: datetime?, provenance: string}
    TemporalInterval: {start: datetime, end: datetime, time_frame: string, precision: string?}
    CausalClaim: {id: string, cause: string, effect: string, mechanism: string?, context: string?, confidence: number, evidence: string[], assumptions: string[]}
    Observation: {id: string, observed_at: datetime, source: string, uncertainty: number?}
    Intervention: {id: string, target: string, applied_at: datetime, context: string?, provenance: string}
    Confounder: {id: string, description: string, evidence: string[]}
    Relationship: {id: string, subject: string, predicate: string, object: string, confidence: number, provenance: string}

  predicates:
    precedes(a: Event, b: Event)
    follows(a: Event, b: Event)
    overlaps(a: Event, b: Event)
    causes(a: Event, b: Event)
    contributes_to(a: Event, b: Event)
    mediates(m: Event, cause: Event, effect: Event)
    confounds(c: Confounder, cause: Event, effect: Event)
    intervenes_on(i: Intervention, target: string)
    supported_by(claim: CausalClaim, observation: Observation)

  constraints:
    - "All relationship endpoints must resolve to typed entities."
    - "Confidence must be within [0, 1]."
    - "Normalise timestamps to an explicit time standard while retaining original offsets and precision."
    - "For strict precedence, end(a) < start(b); do not assume unknown boundaries."
    - "Temporal precedence alone never establishes causation."
    - "Correlation, co-occurrence and sequence alone never establish causation."
    - "Causal claims must record evidence, assumptions, context and plausible confounders."
    - "Causation is not automatically transitive; mediation and context must be assessed."
    - "Do not infer that causes are necessary, sufficient or exclusive without supporting evidence."
    - "Respect temporal resolution and uncertainty when determining event order."

  inference:
    rule inverse_precedence:
      if precedes(a,b)
      then follows(b,a)
      confidence: "retain original evidence and temporal precision"
    rule precedence_transitivity:
      if precedes(a,b) and precedes(b,c) and compatible_time_frames(a,b,c)
      then precedes(a,c)
      confidence: "propagate temporal uncertainty without increasing certainty"
    rule causal_temporal_consistency:
      if causes(a,b) and known_intervals(a,b) and incompatible_with_causal_order(a,b)
      then flag_for_review(a,b)
      confidence: "do not silently delete the causal claim"
    rule supported_causal_hypothesis:
      if causal_evidence(a,b) and mechanism_consistent(a,b) and confounders_assessed(a,b)
      then candidate_causal_claim(a,b)
      confidence: "calibrate to study design and independent evidence; do not automatically assert causes(a,b)"

  SEMANTIC_EQUIVALENCE:
    - "Distinguish temporal precedence, causal contribution, intervention, mediation and correlation."
    - "Resolve event aliases only when identity, temporal scope and provenance agree."
    - "Preserve context when mapping causal terminology across scientific domains."

  CONFIDENCE:
    range: [0,1]
    factors: [temporal_precision, source_reliability, causal_identification, confounding_control, mechanism_support, replication]
    policy: "No confidence inflation from chained rules or repeated dependent observations."

  VALIDATION:
    - "Check endpoint types, interval ordering, timestamp precision and reference frames."
    - "Check provenance, study design, assumptions and possible confounders."
    - "Flag temporal contradictions and unsupported causal assertions."
    - "Preserve alternative causal models when evidence is inconclusive."

  FLOW_HASH:
    algorithm: "SHA-256"
    input: "canonicalised ontology version, typed assertions, rules, temporal context and provenance"
    note: "Compute at execution; no hash value is claimed here."

  UPDATE_MODEL:
    strategy: "versioned, provenance-preserving incremental update"
    steps: [ingest, normalise, validate, compare, reconcile, reassess_causal_claims, recompute_inferences, publish]
    rollback: true

  AI_CONSUMPTION:
    representation: "typed temporal-causal knowledge graph with evidence-backed relationships"
    outputs: [temporal_edges, causal_hypotheses, confidence_annotations, confounder_links, provenance_records, validation_flags]
    safeguards: [no_post_hoc_causal_inference, preserve_temporal_uncertainty, no_unsupported_transitivity, explicit_evidence]
}

END FLOW ALGEBRA