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 25 篇，第 1 / 1 页

1. ICML 2023Active causal structure learning with advice
2. ICML 2023Active Learning based Structural Inference
3. ICML 2023Approximate Causal Effect Identification under Weak Confounding
4. ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
5. ICML 2023Causal Bounds in Quasi-Markovian Graphs
6. ICML 2023Causal Isotonic Calibration for Heterogeneous Treatment Effects
7. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
8. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
9. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
10. ICML 2023Collaborative Causal Inference with Fair Incentives
11. ICML 2023Conditions and Assumptions for Constraint-based Causal Structure Learning
12. ICML 2023Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
13. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
14. ICML 2023Covariate balancing using the integral probability metric for causal inference
15. ICML 2023Estimating Causal Effects using a Multi-task Deep Ensemble
16. ICML 2023Estimating Possible Causal Effects with Latent Variables via Adjustment
17. ICML 2023Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space
18. ICML 2023Linear Causal Disentanglement via Interventions
19. ICML 2023Neural Algorithmic Reasoning with Causal Regularisation
20. ICML 2023On Data Manifolds Entailed by Structural Causal Models
21. ICML 2023On the Relationship Between Explanation and Prediction: A Causal View
22. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
23. ICML 2023Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
24. ICML 2023Towards Trustworthy Explanation: On Causal Rationalization
25. ICML 2023Which Invariance Should We Transfer? A Causal Minimax Learning Approach

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。