Why did a governed system fail? What caused an autonomous agent to take an action? If a synthetic organism produces an output, what chain of events led to that output, and what would have happened if a single link in that chain had been different? The DAIGS ecosystem now governs matter (Quantum), time (Chronos), space (Dimensional), and identity (Identity) — but none of these substrates answers the why question. They record what happened, when, where, and to whom, but not why. This paper introduces Lume-Causal, a deterministic substrate for causal governance. Lume-Causal defines causality as a governed primitive — not a statistical correlation, not a post-hoc explanation, but a certified, invariant-enforced, policy-governed causal graph managed by the Lume runtime. Every cause-effect relationship is indexed by a CausalIndex, recorded in the Causal Chain (C-Chain), and certified by the Causal Certificate Authority. I formalize the Causal model, define seven causal invariants, specify four certificate types, present the Causal Inference Engine (which traces cause-effect chains deterministically), and present the Counterfactual Engine (which evaluates "what would have happened if?" questions with governed, certified results). With Lume-Causal, the five-chain Physics Substrate Layer is complete: every governed event in the DAIGS ecosystem now has full provenance — what happened (Q-Chain), when (T-Chain), where (D-Chain), who (I-Chain), and why (C-Chain).
Ronald Jason Andrews (Thu,) studied this question.