Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the underlying causal network is known a priori - an assumption rarely satisfied in real-world domains where causal structure is uncertain, evolving, or only indirectly observable. This limits the applicability of dynamic causal inference in many scientific settings. We propose Dynamic Causal Network Autoregression (DCNAR), a two-stage neural causal modeling framework that integrates data-driven causal discovery with time-varying causal inference. In the first stage, a neural autoregressive causal discovery model learns a sparse directed causal network from multivariate time series. In the second stage, this learned structure is used as a structural prior for a time-varying neural network autoregression, enabling dynamic estimation of causal influence without requiring pre-specified network structure. We evaluate the scientific validity of DCNAR using behavioral diagnostics that assess causal necessity, temporal stability, and sensitivity to structural change, rather than predictive accuracy alone. Experiments on multi-country panel time-series data demonstrate that learned causal networks yield more stable and behaviorally meaningful dynamic causal inferences than coefficient-based or structure-free alternatives, even when forecasting performance is comparable. These results position DCNAR as a general framework for using AI as a scientific instrument for dynamic causal reasoning under structural uncertainty.

翻译：时变因果模型为研究动态科学系统提供了强大框架，但现有方法大多假设底层因果网络已知——这一假设在现实领域中难以满足，因为因果结构通常不确定、演化或仅能间接观测。这限制了动态因果推断在诸多科学场景中的适用性。我们提出动态因果网络自回归模型（DCNAR），这是一种两阶段神经因果建模框架，将数据驱动的因果发现与时变因果推断相结合。第一阶段，神经自回归因果发现模型从多元时间序列中学习稀疏有向因果网络；第二阶段，将习得结构作为时变神经网络自回归的结构先验，从而无需预设网络结构即可实现因果影响的动态估计。我们通过评估因果必要性、时间稳定性及结构变化敏感性的行为诊断指标，而非仅依赖预测准确性，验证了DCNAR的科学有效性。多国面板时间序列数据实验表明，即使预测性能相当，基于学习因果网络的方法比系数驱动或无结构方法能产生更稳定且更具行为意义的动态因果推断。这些结果确立了DCNAR作为通用框架的地位，可在结构不确定性下将AI用作动态因果推理的科学工具。