UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems

• URL: http://arxiv.org/abs/2511.03168v1
• Date: Wed, 05 Nov 2025 04:34:31 GMT
• Title: UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems
• Authors: Tingzhu Bi, Yicheng Pan, Xinrui Jiang, Huize Sun, Meng Ma, Ping Wang,
• Abstract summary: We propose UnCLe, a novel deep learning method for scalable dynamic causal discovery.<n>UnCLe employs a pair of Uncoupler and Recoupler networks to disentangle input time series into semantic representations.<n>It estimates dynamic causal influences by analyzing datapoint-wise prediction errors induced by temporal perturbations.
• Score: 4.9593603893289115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncovering cause-effect relationships from observational time series is fundamental to understanding complex systems. While many methods infer static causal graphs, real-world systems often exhibit dynamic causality-where relationships evolve over time. Accurately capturing these temporal dynamics requires time-resolved causal graphs. We propose UnCLe, a novel deep learning method for scalable dynamic causal discovery. UnCLe employs a pair of Uncoupler and Recoupler networks to disentangle input time series into semantic representations and learns inter-variable dependencies via auto-regressive Dependency Matrices. It estimates dynamic causal influences by analyzing datapoint-wise prediction errors induced by temporal perturbations. Extensive experiments demonstrate that UnCLe not only outperforms state-of-the-art baselines on static causal discovery benchmarks but, more importantly, exhibits a unique capability to accurately capture and represent evolving temporal causality in both synthetic and real-world dynamic systems (e.g., human motion). UnCLe offers a promising approach for revealing the underlying, time-varying mechanisms of complex phenomena.

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