深度学习 · causal inference论文趋势 2023–2026

主要结论

• 深度学习 · causal inference论文从 2023 年的 108 篇降到 2026 年的 66 篇，热度在回落。
• NeurIPS 是发深度学习 · causal inference论文最多的会议，2023–2026 年共 196 篇，占四个会议总量的 62%。
• 交叉方向里涨得最快的是因果推荐：同时属于两个主题的论文从 2023 年的 27 篇增至 2026 年的 55 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 10；同组增长最快的扩散模型是 4.1 倍，深度学习 · causal inference是 0.6 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
• ICML 2026Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
• ICLR 2026Causal-Steer: Disentangled Continuous Style Control without Parallel Corpora
• CVPR 2026Breaking Spurious Correlations Uncertainty-Driven Causal Transformers for AU Detection
• NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
• ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
• ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
• CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
• NeurIPS 2024A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding
• ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
• ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
• CVPR 2024Bi-Causal: Group Activity Recognition via Bidirectional Causality
• NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
• ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
• ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
• CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
• NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
• ICML 2026Caracal: Causal Architecture via Spectral Mixing
• ICLR 2026Counterfactual Structural Causal Bandits
• CVPR 2026Bridge Basis-Driven Causal Inference Marries VFMs for Domain Generalization

全部深度学习 · causal inference论文

共 26 篇，第 1 / 1 页

1. ICLR 2026Addressing divergent representations from causal interventions on neural networks
2. ICLR 2026Causal-Steer: Disentangled Continuous Style Control without Parallel Corpora
3. ICLR 2026Coarse-to-Fine Learning of Dynamic Causal Structures
4. ICLR 2026Contextual Causal Bayesian Optimisation
5. ICLR 2026Counterfactual Structural Causal Bandits
6. ICLR 2026Exploratory Causal Inference in SAEnce
7. ICLR 2026On Measuring Influence in Avoiding Undesired Future
8. ICLR 2026Topological Causal Effects
9. ICLR 2025A Robust Method to Discover Causal or Anticausal Relation
10. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
11. ICLR 2025Doubly robust identification of treatment effects from multiple environments
12. ICLR 2025Incremental Causal Effect for Time to Treatment Initialization
13. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
14. ICLR 2025Standardizing Structural Causal Models
15. ICLR 2024Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
16. ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
17. ICLR 2024Causally Aligned Curriculum Learning
18. ICLR 2024Debiased Collaborative Filtering with Kernel-Based Causal Balancing
19. ICLR 2024Learning in reverse causal strategic environments with ramifications on two sided markets
20. ICLR 2024Treatment Effects Estimation By Uniform Transformer
21. ICLR 2023Causal Balancing for Domain Generalization
22. ICLR 2023Combinatorial Pure Exploration of Causal Bandits
23. ICLR 2023Estimating individual treatment effects under unobserved confounding using binary instruments
24. ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
25. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
26. ICLR 2023Learning to Induce Causal Structure

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · causal inference论文？

共 316 篇，按年份分别是 2023 年 108 篇、2024 年 108 篇、2025 年 34 篇、2026 年 66 篇。

NeurIPS 2026 有多少篇深度学习 · causal inference论文？

NeurIPS 2026 有 28 篇深度学习 · causal inference论文，上一届是 9 篇。

ICLR 2026 有多少篇深度学习 · causal inference论文？

ICLR 2026 有 8 篇深度学习 · causal inference论文，上一届是 6 篇。

ICML 2026 有多少篇深度学习 · causal inference论文？

ICML 2026 有 26 篇深度学习 · causal inference论文，上一届是 18 篇。

CVPR 2026 有多少篇深度学习 · causal inference论文？

CVPR 2026 有 4 篇深度学习 · causal inference论文，上一届是 1 篇。

哪个会议的深度学习 · causal inference论文最多？

NeurIPS，2023–2026 年共 196 篇。