Layered Event Modelling: Semantic Separation as a Precondition for Explicit Causal Analysis

Modelling in applied risk and safety disciplines is limited less by the availability of data than by the semantic condition of the vocabulary used. Where a single predicate — integrity, failure, compromise, availability — is applied across physical assemblies, executing systems, representations and organisational outcomes, the model cannot support explicit causal analysis: the predicate silently changes its truth conditions as the analysis crosses abstraction boundaries, and the crossing itself becomes invisible. The central claim is narrower and, we argue, more defensible than a general thesis about causal reasoning: semantic continuity across an abstraction boundary conceals the mechanism by which the boundary is crossed. Causal inference remains possible with ambiguous vocabulary; what becomes impossible is an explicit, testable model of the transition. Four contributions follow. First, we separate the framework's object language from its metalanguage, so that semantic separation constrains domain predicates without prohibiting the shared formal vocabulary any model requires. Second, we distinguish five structures routinely fused into a single "layer stack": a substrate hierarchy, an event-abstraction hierarchy, a representation hierarchy, an epistemic access structure, and a normative structure of rights, obligations and prohibitions. Third, we propose a taxonomy of six edge types for token-level event structure — causal, constitutive, classificatory, normative, representational and evidential — kept apart from a type-level stratum of architectural dependencies and the normative meta-layer, and argue that a transition duration Δt is defined only for causal edges. Fourth, we give operational diagnostics for predicates and for edges, so that the proposal is falsifiable rather than hortatory. Cyber, operational and compliance risk are the primary instantiations, and the cause-oriented Top Level Cyber Threat Clusters (TLCTC) framework serves as the worked instantiation throughout. Generality beyond them is treated as a hypothesis, not a result. In bow-tie terms, the framework gives the consequence side — the passage from system risk events through data risk events to business risk events — the discipline a cause-based taxonomy already gives the cause side. Its most consequential practical claim is that a transition duration, and with it the intervention window available to detective, response and circuit-breaker controls, exists only on causal edges, and is undefined rather than zero on constitutive, classificatory and normative ones.