DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series
DoTime：干预与反事实时间序列的合成基准生成器AI 解读 ↓
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DoTime：干预与反事实时间序列的合成基准生成器
- 研究问题
- 如何构造可扩展且具有精确真值的时间序列基准，以评估干预和反事实估计，并检验干预训练相对观察性训练的作用？
- 方法与思路
- 作者构建多元时间结构因果模型生成器，加入连续时间干预窗口、反事实采样与机制切换，并提供冻结评估集及基线框架。
- 材料陈述的贡献
- 摘要称，基准覆盖八类识别结构和非平稳动态；在结构匹配的留出评估中，干预训练网络的方向准确性优势在所测设置均为正。
- 阅读关注点
- 应核对各识别结构、反事实共享噪声和重叠保护的定义，以及训练评估匹配程度、容量控制、随机种子和真值生成方式。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实。包及评估集的发布未独立核验，结构匹配的合成结果不能直接外推到真实时间序列。
原文摘要
Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science. We introduce \textbf{DoTime}, an open, scalable, and theoretically grounded generator of multivariate temporal structural causal models (TSCMs) with interventions, released as the \code{dotime} PyPI package together with four frozen evaluation suites. Beyond existing work, it adds capabilities absent from prior generators: continuous-time intervention \emph{windows}, counterfactual sampling modes with a positivity guard, regime-switching SCMs as a strict generalization of interrupted time series, non-stationary dynamics by construction with switching SCM parameters, and deterministic ramp and sinusoidal intervention profiles that place trends and structural breaks \emph{inside} the evaluation window. Moreover, it demonstrates the suitability of the generator as a prior for a causal foundation model reference implementation. The released suites span a training-scale snapshot of $100{,}000$ trajectories and eight named identification structures, each with exact ground truth: paired interventional trajectories from the same SCM throughout, and shared-noise counterfactuals in the continuous-time suite. We ship reference baseline implementations with an evaluation harness, and pose a falsifiable claim: interventional training buys a measurable direction-accuracy advantage over an observational model of identical capacity. It is tested across three training seeds per arm. Under structure-matched evaluation on held-out episodes, the interventional prior-fitted network's (PFN) gap is positive in every structure, trajectory length, and seed tested.