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

2025-11-14

AI Summary

This work addresses the scarcity of ground-truth causal annotations and limited physiological data in multivariate time series (MTS) research. To overcome these challenges, we propose a practical spatiotemporal causal graph modeling framework grounded in discrete-time structural causal processes. The framework supports lagged causality, mixed variable types (continuous and discrete), and interpretable causal interventions under user-specified distribution shifts. It integrates domain expert knowledge with algorithmic suggestions to enable programmable causal graph construction and interactive modeling workflows. Edge functions are implemented via a hybrid design combining neural networks with parameterized templates, explicitly encoding causal dynamics. The resulting synthetic MTS datasets feature known, intervenable causal structures—significantly augmenting real-world datasets and providing a configurable, reproducible benchmark for evaluating causal discovery algorithms.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Bridging structured and unstructured data

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.

Problem

• Simulates multivariate time series with known causal dynamics
• Augments real datasets using expert knowledge and algorithms
• Enables validation of causal discovery methods through flexible simulation

Innovation

• Interactive framework for spatiotemporal causal graphical models
• Generates synthetic time series with known causal dynamics
• Combines expert knowledge with algorithmic proposals in workflow