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 150 篇，第 1 / 3 页

  1. NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
  2. NeurIPS 2026Act, Validate, Adapt: Closing the Causal Discovery-Control Loop
  3. NeurIPS 2026AgentCBO: Causal Belief Routing for Bayesian Optimization under Unknown Graphs
  4. NeurIPS 2026Attention-Based Pretraining for Unsupervised Amortized Causal Discovery
  5. NeurIPS 2026Automated Causal Effect Estimation through Self-Evolving AI
  6. NeurIPS 2026Causal Abstractions, Categorically Unified
  7. NeurIPS 2026Causal Discovery from Unseen Environments
  8. NeurIPS 2026Causal discovery needs explicit epistemic standards
  9. NeurIPS 2026Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach
  10. NeurIPS 2026Causal Discovery under Time-Varying Delays
  11. NeurIPS 2026Causal Discovery via Transformed Low-Rank Quantile Surfaces
  12. NeurIPS 2026Causal Discovery with False Positive Error Control
  13. NeurIPS 2026Causal Gradient Steering: Exposing Shortcut Gradients by Destroying Causal Signal
  14. NeurIPS 2026Causal learning with the invariance principle
  15. NeurIPS 2026Causal Representation Learning for Generalisable Recommendation
  16. NeurIPS 2026Causal Survival Forests with Negative Controls
  17. NeurIPS 2026CausalTab: Pretraining Across Causal Environments for Tabular Causal Discovery
  18. NeurIPS 2026Coarsening Linear Non-Gaussian Causal Models with Cycles
  19. NeurIPS 2026Complementing DINO Features with Image Structure for Part Discovery
  20. NeurIPS 2026Cross-Model Circuit Discovery
  21. NeurIPS 2026Decision Path Tracing for Causal Analysis in Transformers
  22. NeurIPS 2026Decomposing Effects in Neural Causal Models
  23. NeurIPS 2026Effect-Level Validation for Causal Discovery in Interactive Telemetry
  24. NeurIPS 2026Emergent representations of graphical structure in mechanistic neural models of causal judgment
  25. NeurIPS 2026Fiedler-Regularized Causal Discovery for Sparse Connected DAGs
  26. NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
  27. NeurIPS 2026Generalized Bayes for Causal Inference
  28. NeurIPS 2026Identifying Structural Biases from Causal Mechanism Shifts
  29. NeurIPS 2026Improving Causal Explanations
  30. NeurIPS 2026Improving constraint-based discovery with robust propagation and LLM priors
  31. NeurIPS 2026Incorporating Neural Network Structure in the Bayesian Learning Rule
  32. NeurIPS 2026Integrating Background Knowledge for Scalable Causal Discovery
  33. NeurIPS 2026Learning Causal Orderings for In-Context Tabular Prediction
  34. NeurIPS 2026Mapping Uncharted Symmetries: Machine Discovery in Combinatorics
  35. NeurIPS 2026MOCHA: Discovering Multi-Order Dynamic Causal Structure in Temporal Point Processes
  36. NeurIPS 2026MOSAIC: Module Discovery via Sparse Additive Identifiable Causal Learning for Scientific Time Series
  37. NeurIPS 2026Neural Causal Models under Markov Equivalence
  38. NeurIPS 2026PACO: Partial-order-Augmented Continuous Optimization for Differentiable Causal Discovery
  39. NeurIPS 2026PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
  40. NeurIPS 2026Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs
  41. NeurIPS 2026Reusable Conditional Resampling via Flow Matching for Constraint-Based Causal Discovery
  42. NeurIPS 2026RnR: a meta-solver for causal discovery in undersampled time series data
  43. NeurIPS 2026SCDM: Scalable Causal Discovery in Nonlinear Temporal Systems with Meta-Learning
  44. NeurIPS 2026Seen-Constrained Model-Order Selection for Unknown-$K$ Generalized Category Discovery
  45. NeurIPS 2026Stable Partial Order Constraints for Temporal Causal Structure Learning
  46. NeurIPS 2026Statistical Inference in Causal Partial Identification under Smooth Densities
  47. NeurIPS 2026Structural Causal Bottleneck Models
  48. NeurIPS 2026Towards a Universal Causal Reasoner
  49. NeurIPS 2026Two-Clustering Regime of Token Dynamics in Causal Attention
  50. NeurIPS 2026Underlying Functional Structure of Reinforcement 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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