Causal Discovery with Inverted Self-attention for Multivariate Time Series

arXiv:2607.28212 · cs.CL, cs.LG · Submitted 2026-07-30 · Read on arXiv

Yusen Liu, Yong Wang, Yifan Yin, Tianqing Zhu, Xiufeng Liu, Huan Huo

cs.CL, cs.LG

Submitted: 2026-07-30

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Causal Discovery — 35× in this paper · explained in 3 episodes
Attention Mechanism — 22× in this paper · explained in 3 episodes
Transformer — 12× in this paper · explained in 2 episodes
Sparsity — 11× in this paper · explained in 5 episodes
Verification — 7× in this paper · explained in 2 episodes
Ablation — 6× in this paper · explained in 1 episode
Interpretability — 4× in this paper · explained in 15 episodes
Robustness — 4× in this paper · explained in 6 episodes
Transformer Architecture — 4× in this paper · explained in 4 episodes
Ablation Studies — 3× in this paper · explained in 1 episode
Ablation Study — 3× in this paper · explained in 3 episodes
Benchmark — 3× in this paper · explained in 2 episodes
Causal Inference — 3× in this paper · explained in 5 episodes
Permutation Importance — 3× in this paper
Accuracy — 2× in this paper · explained in 1 episode
Conditional Independence — 2× in this paper · explained in 2 episodes
Faithfulness — 2× in this paper · explained in 2 episodes
Tokenization — 2× in this paper · explained in 2 episodes
Transformers — 2× in this paper · explained in 2 episodes
Adam — 1× in this paper · explained in 3 episodes
Directed Acyclic Graph (DAG) — 1× in this paper · explained in 2 episodes
Disentanglement — 1× in this paper · explained in 2 episodes
Kernel Granger Causality — 1× in this paper
Loss Function — 1× in this paper · explained in 2 episodes
Overfitting — 1× in this paper · explained in 2 episodes
Time Series Fast Causal Inference — 1× in this paper
Time Series Forecasting — 1× in this paper · explained in 4 episodes
Transformer Model — 1× in this paper · explained in 2 episodes

Terminology

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