Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series remains limited by the absence of synthetic data generators that provide interventional targets. Existing time series benchmarks generate observational data with ground-truth causal graphs but lack the interventional data required for training causal foundation models. To address this, we propose \textbf{CausalTimePrior}, a principled framework for generating synthetic temporal structural causal models (TSCMs) with paired observational and interventional time series. Our prior supports configurable causal graph structures, nonlinear autoregressive mechanisms, regime-switching dynamics, and multiple intervention types (hard, soft, time-varying). We demonstrate that PFNs trained on CausalTimePrior can perform in-context causal effect estimation on held-out TSCMs, establishing a pathway toward foundation models for time series causal inference.

翻译：先验数据拟合网络（PFNs）已成为表格因果推断的强大基础模型，但其向时间序列的扩展因缺乏提供干预目标的合成数据生成器而受到限制。现有时间序列基准生成带有真实因果图的观测数据，但缺乏训练因果基础模型所需的干预数据。为此，我们提出**CausalTimePrior**，一个用于生成具有配对观测和干预时间序列的合成时间序列结构因果模型（TSCMs）的原则性框架。我们的先验支持可配置的因果图结构、非线性自回归机制、机制转换动态以及多种干预类型（硬干预、软干预、时变干预）。我们证明，在CausalTimePrior上训练的PFNs能够对未见的TSCMs进行上下文因果效应估计，为时间序列因果推断的基础模型建立了一条路径。