KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamics
This provides a tool for researchers in causal inference and time series analysis to augment real-world datasets and test algorithms under controlled conditions, though it is incremental as it builds on existing simulation and causal modeling concepts.
The authors tackled the challenge of limited access to physiological multivariate time series data by developing KarmaTS, a platform that generates synthetic data with known causal dynamics, enabling flexible validation and benchmarking of causal discovery algorithms through expert-informed simulation.
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.