构建可交互的多变量时间序列因果模拟平台，支持专家知识与算法协同生成合成数据。
KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamics
- 采用人机协作流程，融合专家知识与算法生成离散时间因果过程。
- 支持含滞后与同期关系的混合变量类型模拟，可施加用户指定的分布偏移干预。
- 适用于因果发现算法的验证与基准测试，特别适合生理等受限数据场景。
我们提出KarmaTS，一个用于多变量时间序列（MTS）模拟的交互式框架，能够构建具有时滞索引的可执行时空因果图模型。针对难以获取的生理数据挑战，KarmaTS生成具备已知因果动态的合成数据，并通过专家知识增强真实数据集。系统通过人机协同工作流，结合专家知识与算法建议，构建离散时间结构因果过程（DSCP），支持仿真与因果干预，包括用户自定义的分布偏移情形。KarmaTS能处理混合变量类型、同期与滞后边，以及从可参数化模板到神经网络模型的模块化边函数。这些特性共同实现了基于专家先验的因果发现算法灵活验证与基准测试。
原文摘要 · Abstract (English)
We introduce KarmaTS, an interactive framework for constructing lag-indexed, executable spatiotemporal causal graphical models for multivariate time series (MTS) simulation. Motivated by the challenge of access-restricted physiological data, KarmaTS generates synthetic MTS with known causal dynamics and augments real-world datasets with expert knowledge. The system constructs a discrete-time structural causal process (DSCP) by combining expert knowledge and algorithmic proposals in a mixed-initiative, human-in-the-loop workflow. The resulting DSCP supports simulation and causal interventions, including those under user-specified distribution shifts. KarmaTS handles mixed variable types, contemporaneous and lagged edges, and modular edge functionals ranging from parameterizable templates to neural network models. Together, these features enable flexible validation and benchmarking of causal discovery algorithms through expert-informed simulation.
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