Do frontier LLMs reason causally, or do they pattern-match, yielding under pressure and hedging under uncertainty? We frame causal judgment as evaluation along three axes, Utility, Safety, and Wise Refusal, across Pearl’s Ladder. We introduce Recursive Causal Audit (RCA), a process-integrity evaluator whose Judge has no access to gold labels: it checks whether a model’s answer is entailed by itsown derivation, internally consistent, and not dominated by user hints under pressure. RCA unifies persona and pressure: prompt tone is the intervention that regulates pressure-induced drift. For fine diagnostic resolution we use CAUSALT3, with explicit trap families and standardized pressure protocols. CAUSALT3 reveals a Skepticism Trap (Claude Haiku rejects 60% of valid L1 links) and a Scaling Paradox (GPT-5.2 underperforms GPT-4-Turbo by 55 points on L3, driven by paralysis rather than hallucination). Under RCA, operating points shift toward the high-Utility, high-Safety quadrant without retraining, consistent with much of the observed failure arising from how answers are rendered under pressure rather than from missing causal knowledge.