Causal dynamic decision-making for robotic systems in non-Markovian high-difficulty surgery

Markov assumption-based surgical decision models cannot account for the time-varying, irregular effects of high-risk intraoperative anomalies such as sudden hemorrhage or inadvertent instrument loss, making them inadequate for specialized procedures like neurosurgery and spinal interventions. To overcome the non-Markovian limitations of conventional surgical process modeling, this study develops a causal modeling framework based on Vector Autoregression (VAR) and Granger causality analysis. The framework constructs a causal chain (original gesture $S_i$ → abnormal event $E_j$ → recovery action $Z_k$ ) to enable intelligent response and adaptive decision-making. Validation was performed on a large-scale synthetic dataset containing 10,000 samples (including anomaly, positive, and negative cases), and evaluated using accuracy, F1-score, and recall metrics. Experimental results show the proposed method achieves 95.60% accuracy in causal inference, maintaining stability at 10,000 samples with an F1 score of 95.77%. Notably, recall (95.88%) slightly exceeds precision (95.34%), reflecting the clinical principle of prioritizing safety. The framework effectively captures non-Markovian temporal correlations induced by abnormal events, overcoming key limitations of traditional approaches. Its design is not procedure-specific, providing a versatile and generalizable pathway for enhancing autonomous decision-making in surgical robots across diverse clinical applications.