因果发现论文趋势 2023–2026

结构学习

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 379 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

因果发现论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023271016659
2024361332182
2025342731799
20265325547139

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

全部因果发现论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 379 篇，第 8 / 8 页

  1. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
  2. ICML 2023Sequential Monte Carlo Learning for Time Series Structure Discovery
  3. NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
  4. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
  5. NeurIPS 2023Assumption violations in causal discovery and the robustness of score matching
  6. NeurIPS 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
  7. NeurIPS 2023Bayesian Active Causal Discovery with Multi-Fidelity Experiments
  8. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
  9. NeurIPS 2023Causal discovery from observational and interventional data across multiple environments
  10. NeurIPS 2023Causal Discovery from Subsampled Time Series with Proxy Variables
  11. NeurIPS 2023Causal Discovery in Semi-Stationary Time Series
  12. NeurIPS 2023Causal Effect Identification in Uncertain Causal Networks
  13. NeurIPS 2023Causal Imitability Under Context-Specific Independence Relations
  14. NeurIPS 2023Causal Interpretation of Self-Attention in Pre-Trained Transformers
  15. NeurIPS 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
  16. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets
  17. NeurIPS 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
  18. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
  19. NeurIPS 2023Identification of Nonlinear Latent Hierarchical Models
  20. NeurIPS 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
  21. NeurIPS 2023Learning Causal Models under Independent Changes
  22. NeurIPS 2023Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
  23. NeurIPS 2023Meek Separators and Their Applications in Targeted Causal Discovery
  24. NeurIPS 2023Rotating Features for Object Discovery
  25. NeurIPS 2023Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling
  26. NeurIPS 2023Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
  27. NeurIPS 2023Towards Distribution-Agnostic Generalized Category Discovery
  28. NeurIPS 2023Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
  29. NeurIPS 2023Unpaired Multi-Domain Causal Representation Learning

在 Claude、Cursor 里直接问这个趋势

接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。

可以直接复制这句话问 AI

用 paperlist 查因果发现在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "causal-discovery"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

常见问题

2023–2026 年四大会议一共有多少篇因果发现论文？
共 379 篇，按年份分别是 2023 年 59 篇、2024 年 82 篇、2025 年 99 篇、2026 年 139 篇。
NeurIPS 2026 有多少篇因果发现论文？
NeurIPS 2026 有 53 篇因果发现论文，上一届是 34 篇。
ICLR 2026 有多少篇因果发现论文？
ICLR 2026 有 25 篇因果发现论文，上一届是 27 篇。
ICML 2026 有多少篇因果发现论文？
ICML 2026 有 54 篇因果发现论文，上一届是 31 篇。
CVPR 2026 有多少篇因果发现论文？
CVPR 2026 有 7 篇因果发现论文，上一届是 7 篇。
哪个会议的因果发现论文最多？
NeurIPS，2023–2026 年共 150 篇。

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