Decoupled Causal Discovery

arXiv:2609.23535 · cs.LG, stat.ML · Submitted 2026-09-20 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-20

Updated: 2026-09-20

Comments: 24 pages

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

Terminology

• Decoupling — 34× in this paper · explained in 2 episodes
• Causal Discovery — 31× in this paper · explained in 2 episodes
• Causal Inference — 7× in this paper · explained in 4 episodes
• F1 Score — 6× in this paper · explained in 3 episodes
• Decoupled Causal Discovery — 5× in this paper
• Robustness — 5× in this paper · explained in 5 episodes
• Core — 4× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Completed Partially Directed Acyclic Graph — 3× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• Selection Bias — 3× in this paper · explained in 4 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Axioms — 2× in this paper · explained in 2 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Entanglement — 1× in this paper · explained in 3 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Hamming Distance — 1× in this paper · explained in 2 episodes
• Hilbert-Schmidt Independence Criterion — 1× in this paper
• Objective Function — 1× in this paper · explained in 2 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks