Causal audit traces for high-risk AI decisions

A technical brief presenting a causal audit-trace architecture for high-risk AI decisions. The paper introduces causal-credit-risk-engine, a config-driven causal DAG system that generates counterfactuals, replayable audit records, fairness diagnostics, and tamper-evident decision traces to support explainability, oversight, model-risk review, and AI governance workflows.Keywords: causal AI, AI governance, explainable AI, audit traces, model-risk management, counterfactual reasoning, EU AI Act, high-risk AI, deterministic replay, fairness diagnostics