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 133 篇，第 3 / 3 页

  1. ICML 2024Learning Causal Dynamics Models in Object-Oriented Environments
  2. ICML 2024Learning Causal Relations from Subsampled Time Series with Two Time-Slices
  3. ICML 2024Local Causal Structure Learning in the Presence of Latent Variables
  4. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
  5. ICML 2024On Positivity Condition for Causal Inference
  6. ICML 2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
  7. ICML 2024On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
  8. ICML 2024Optimal Kernel Choice for Score Function-based Causal Discovery
  9. ICML 2024Optimal Transport for Structure Learning Under Missing Data
  10. ICML 2024Reducing Balancing Error for Causal Inference via Optimal Transport
  11. ICML 2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
  12. ICML 2024Score-Based Causal Discovery of Latent Variable Causal Models
  13. ICML 2024Stable Differentiable Causal Discovery
  14. ICML 2024StableMask: Refining Causal Masking in Decoder-only Transformer
  15. ICML 2024Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
  16. ICML 2024Transport of Algebraic Structure to Latent Embeddings
  17. ICML 2024Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
  18. ICML 2023Active causal structure learning with advice
  19. ICML 2023Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
  20. ICML 2023Behavior Contrastive Learning for Unsupervised Skill Discovery
  21. ICML 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
  22. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
  23. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
  24. ICML 2023Collaborative Causal Inference with Fair Incentives
  25. ICML 2023Covariate balancing using the integral probability metric for causal inference
  26. ICML 2023Differentiable Multi-Target Causal Bayesian Experimental Design
  27. ICML 2023From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
  28. ICML 2023Interventional Causal Representation Learning
  29. ICML 2023Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
  30. ICML 2023Nonlinear Causal Discovery with Latent Confounders
  31. ICML 2023On Data Manifolds Entailed by Structural Causal Models
  32. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
  33. ICML 2023Sequential Monte Carlo Learning for Time Series Structure Discovery

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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