Causal Inference in Nanosecond Finance: Detecting Spurious Correlations in AI High-Frequency Trading Strategies

Causal Inference in Nanosecond Finance: Detecting Spurious Correlations in AI High-Frequency Trading Strategies This paper explores how artificial intelligence (AI) models used in high-frequency trading (HFT) often exploit spurious correlations—patterns that appear predictive but lack causal grounding—leading to unstable strategies, alpha decay, and systemic risk. At nanosecond scales, these risks are magnified by speed, opacity, and feedback loops. We propose the integration of causal inference frameworks—including structural causal models (SCMs), do-calculus, invariant causal prediction (ICP), and causal representation learning—into HFT research and practice. Using a combination of historical market data, feature engineering, and agent-based simulations, we compare traditional machine learning approaches with causal inference–enhanced models. The results show that while correlational AI achieves slightly higher short-term accuracy, causal models deliver significantly greater robustness across regimes, resistance to alpha decay, and systemic stability. We introduce the Causal Stability Index (CSI), Interventional Fragility Score (IFS), and propose a new SCAB-CIP (Causal Integrity Protocol) to integrate causal validation into the SCAB framework for AI oversight, ensuring trading agents act on causally robust signals. Contributions of the paper include: A theoretical framework distinguishing spurious vs causal signals in nanosecond markets. A methodology for embedding causal reasoning into AI-driven HFT strategies. Simulated case studies demonstrating causal filtering in action. Ethical and regulatory recommendations, including the SCAB-CIP protocol for causal auditing. This work represents one of the first systematic attempts to apply causal inference to nanosecond finance, offering both academic novelty and practical policy relevance. It argues that the next frontier of HFT innovation is not faster correlations, but causally robust AI systems that align profitability with systemic safety.