In this study, we present a novel constraint-based algorithm for causal structure learning specifically designed for nonlinear autoregressive time series. Our algorithm significantly reduces computational complexity compared to existing methods, making it more efficient and scalable to larger problems. We rigorously evaluate its performance on synthetic datasets, demonstrating that our algorithm not only outperforms current techniques, but also excels in scenarios with limited data availability. These results highlight its potential for practical applications in fields requiring efficient and accurate causal inference from nonlinear time series data.

翻译：本研究提出一种专门针对非线性自回归时间序列的约束型因果结构学习新算法。与现有方法相比，该算法显著降低了计算复杂度，使其在处理更大规模问题时具有更高的效率和可扩展性。我们在合成数据集上对其性能进行了严格评估，结果表明该算法不仅优于现有技术，在数据可用性受限的场景下也表现优异。这些发现凸显了该算法在需要从非线性时间序列数据中进行高效精准因果推断的实际应用领域具有重要潜力。