Causal Discovery with Inverted Self-attention for Multivariate Time Series
Causal discovery in multivariate time series data is challenging due to complex interactions, high dimensionality, and nonlinear dependencies among variables. Existing methods often struggle to capture these complexities, resulting in inaccurate causal structures. To address this issue, we propose a novel framework that leverages self-attention mechanisms within the transformer architecture for causal discovery. Our approach introduces a novel inverted causal self-attention mechanism (CSAM) that emphasizes latent and indirect causal relationships by inverting tokens and inducing sparsity in attention scores, focusing on significant causal interactions and reducing spurious correlations. A...
| Authors | Yusen Liu, Yong Wang, Yifan Yin et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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