KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamics

Haixin Li, Yanke Li, Diego Paez-Granados

Keywords: Clinical Data Simulation, Spatiotemporal Causal Models, Privacy-Preserving Synthetic Data, Multivariate Time Series

Track: Proceedings

Abstract:

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.

General Area: Applications and Practice

Specific Subject Areas: HCI & Data Visualization, Dataset Release & Characterization, Causal Inference & Discovery, Time Series

Data And Code Availability: No

Ethics Board Approval: No

Entered Conflicts: I confirm the above

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Submission Number: 282