Tensor-based second-order causal discovery

arXiv:2606.18074 · stat.ML, cs.LG, stat.ME · Submitted 2026-06-16 · Read on arXiv

Nathan Ouyang, Kexin Wang, Anna Seigal

stat.ML, cs.LG, stat.ME

Submitted: 2026-06-16

Comments: 27 pages, 7 figures. Code available at https://github.com/QWE123665/Tensor-based-Second-order-Causal-Discovery Replacement: Corrected typo in author's name

Code: https://github.com/QWE123665/Tensor-based-Second-order-Causal-Discovery

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

Terminology

• Causal Discovery — 34× in this paper · explained in 3 episodes
• F1 Score — 19× in this paper · explained in 4 episodes
• Tensor-based Second-order Causal Discovery — 17× in this paper
• Conditional Independence — 7× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Causal Inference — 4× in this paper · explained in 5 episodes
• Lasso — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Computational Efficiency — 3× in this paper · explained in 2 episodes
• Directed Acyclic Graph (DAG) — 3× in this paper · explained in 2 episodes
• Non-Gaussianity — 3× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Higher-order statistics — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 5 episodes
• Ablation Study — 1× in this paper · explained in 3 episodes
• Clarity — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Disentanglement — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 5 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Feedback Loop — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Independent Component Analysis — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Orthogonal Projection — 1× in this paper · explained in 2 episodes
• Parameter Identifiability — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 6 episodes

Sources

• Multi-context principal component analysis
• Multi-subspace power method for decomposing partially symmetric tensors

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