因果发现论文趋势 2023–2026

主要结论

• 因果发现论文从 2023 年的 59 篇增长到 2026 年的 139 篇，三年间扩大到约 2.4 倍。
• NeurIPS 是发因果发现论文最多的会议，2023–2026 年共 150 篇，占四个会议总量的 40%。
• 交叉方向里涨得最快的是通用机器学习 · unsupervised and semisupervised learning：同时属于两个主题的论文从 2023 年的 3 篇增至 2026 年的 10 篇。
• 在「因果与公平」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的偏见缓解是 1.5 倍，因果发现是 2.4 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 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
• ICML 2026A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
• ICLR 2026Bures Generalized Category Discovery
• CVPR 2026Learning by Analogy A Causal Framework for Compositional Generalization
• NeurIPS 2025Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
• ICML 2025Bayesian Active Learning for Bivariate Causal Discovery
• ICLR 2025A Conditional Independence Test in the Presence of Discretization
• CVPR 2025Hyperbolic Category Discovery
• NeurIPS 2024Causal Bandits for Linear Structural Equation Models
• ICML 2024Adaptive Online Experimental Design for Causal Discovery
• ICLR 2024A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
• CVPR 2024Contrastive Mean-Shift Learning for Generalized Category Discovery
• NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
• ICML 2023Active causal structure learning with advice
• ICLR 2023Boosting Causal Discovery via Adaptive Sample Reweighting
• CVPR 2023Intrinsic Physical Concepts Discovery With Object-Centric Predictive Models
• NeurIPS 2026Act, Validate, Adapt: Closing the Causal Discovery-Control Loop
• ICML 2026Causal Discovery for Irregularly Time Series with Consistency Guarantees
• ICLR 2026Causal Discovery in the Wild: A Voting-Theoretic Ensemble Approach
• CVPR 2026PlanaReLoc Camera Relocalization in 3D Planar Primitives via Region-Based Structure

全部因果发现论文

共 25 篇，第 1 / 1 页

1. ICLR 2026Bures Generalized Category Discovery
2. ICLR 2026CARL: Preserving Causal Structure in Representation Learning
3. ICLR 2026Causal Discovery in the Wild: A Voting-Theoretic Ensemble Approach
4. ICLR 2026Causal Discovery via Quantile Partial Effect
5. ICLR 2026Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks
6. ICLR 2026Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
7. ICLR 2026Coarse-to-Fine Learning of Dynamic Causal Structures
8. ICLR 2026Conditional Independent Component Analysis For Estimating Causal Structure with Latent Variables
9. ICLR 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
10. ICLR 2026Efficient Ensemble Conditional Independence Test Framework for Causal Discovery
11. ICLR 2026Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
12. ICLR 2026Frequency-Domain Better than Time-Domain for Causal Structure Recovery in Dynamical Systems on Networks
13. ICLR 2026How Transformers Learn Causal Structures In-Context: Explainable Mechanism Meets Theoretical Guarantee
14. ICLR 2026Influence without Confounding: Causal Discovery from Temporal Data with Long-term Carry-over Effects
15. ICLR 2026Learning Dynamic Causal Graphs Under Parametric Uncertainty via Polynomial Chaos Expansions
16. ICLR 2026On Measuring Influence in Avoiding Undesired Future
17. ICLR 2026On the identifiability of causal graphs with multiple environments
18. ICLR 2026Query-Specific Causal Graph Pruning Under Tiered Knowledge
19. ICLR 2026Robust Equation Structure learning with Adaptive Refinement
20. ICLR 2026Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
21. ICLR 2026Some Neural Networks Inherently Preserve Subspace Clustering Structure
22. ICLR 2026Structure Learning from Time-Series Data with Lag-Agnostic Structural Prior
23. ICLR 2026TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
24. ICLR 2026Theoretical Guarantees for Causal Discovery on Large Random Graphs
25. ICLR 2026Topological Causal Effects

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。