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 (Sat,) studied this question.