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 59 篇，第 1 / 2 页

1. CVPR 2023Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery
2. CVPR 2023Dynamic Conceptional Contrastive Learning for Generalized Category Discovery
3. CVPR 2023Intrinsic Physical Concepts Discovery With Object-Centric Predictive Models
4. CVPR 2023Level-S$^2$fM: Structure From Motion on Neural Level Set of Implicit Surfaces
5. CVPR 2023On-the-Fly Category Discovery
6. CVPR 2023PartMix: Regularization Strategy To Learn Part Discovery for Visible-Infrared Person Re-Identification
7. ICLR 2023Boosting Causal Discovery via Adaptive Sample Reweighting
8. ICLR 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
9. ICLR 2023Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
10. ICLR 2023CUTS: Neural Causal Discovery from Irregular Time-Series Data
11. ICLR 2023DAG Learning on the Permutahedron
12. ICLR 2023Diffusion Models for Causal Discovery via Topological Ordering
13. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
14. ICLR 2023Improving the imputation of missing data with Markov Blanket discovery
15. ICLR 2023Learning to Induce Causal Structure
16. ICLR 2023Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise
17. ICML 2023Active causal structure learning with advice
18. ICML 2023Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
19. ICML 2023Behavior Contrastive Learning for Unsupervised Skill Discovery
20. ICML 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
21. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
22. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
23. ICML 2023Collaborative Causal Inference with Fair Incentives
24. ICML 2023Covariate balancing using the integral probability metric for causal inference
25. ICML 2023Differentiable Multi-Target Causal Bayesian Experimental Design
26. ICML 2023From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
27. ICML 2023Interventional Causal Representation Learning
28. ICML 2023Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
29. ICML 2023Nonlinear Causal Discovery with Latent Confounders
30. ICML 2023On Data Manifolds Entailed by Structural Causal Models
31. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
32. ICML 2023Sequential Monte Carlo Learning for Time Series Structure Discovery
33. NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
34. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
35. NeurIPS 2023Assumption violations in causal discovery and the robustness of score matching
36. NeurIPS 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
37. NeurIPS 2023Bayesian Active Causal Discovery with Multi-Fidelity Experiments
38. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
39. NeurIPS 2023Causal discovery from observational and interventional data across multiple environments
40. NeurIPS 2023Causal Discovery from Subsampled Time Series with Proxy Variables
41. NeurIPS 2023Causal Discovery in Semi-Stationary Time Series
42. NeurIPS 2023Causal Effect Identification in Uncertain Causal Networks
43. NeurIPS 2023Causal Imitability Under Context-Specific Independence Relations
44. NeurIPS 2023Causal Interpretation of Self-Attention in Pre-Trained Transformers
45. NeurIPS 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
46. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets
47. NeurIPS 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
48. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
49. NeurIPS 2023Identification of Nonlinear Latent Hierarchical Models
50. NeurIPS 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

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。