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

Haixin Li, Yanke Li, Diego Paez-Granados
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:1427-1445, 2026.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-li26b, title = {{KarmaTS}: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamics}, author = {Li, Haixin and Li, Yanke and Paez-Granados, Diego}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {1427--1445}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/li26b/li26b.pdf}, url = {https://proceedings.mlr.press/v297/li26b.html}, 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.} }
Endnote
%0 Conference Paper %T KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamics %A Haixin Li %A Yanke Li %A Diego Paez-Granados %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-li26b %I PMLR %P 1427--1445 %U https://proceedings.mlr.press/v297/li26b.html %V 297 %X 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.
APA
Li, H., Li, Y. & Paez-Granados, D.. (2026). KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamics. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:1427-1445 Available from https://proceedings.mlr.press/v297/li26b.html.

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