因果推荐论文趋势 2023–2026

主要结论

• 因果推荐论文从 2023 年的 83 篇增长到 2026 年的 190 篇，三年间扩大到约 2.3 倍。
• NeurIPS 是发因果推荐论文最多的会议，2023–2026 年共 227 篇，占四个会议总量的 41%。
• 交叉方向里涨得最快的是多模态大模型：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 21 篇。
• 在「推荐与广告」的 5 个主题里，它的 2026 年论文数排第 3；同组增长最快的点击率预测是 2.6 倍，因果推荐是 2.3 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 Causal Decomposition Approach for Fair Contextual Multi-Armed Bandits
• ICLR 2026ActiveCQ: Active Estimation of Causal Quantities
• CVPR 2026Breaking Spurious Correlations Uncertainty-Driven Causal Transformers for AU Detection
• NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
• ICML 2025A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
• ICLR 2025Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
• CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
• NeurIPS 2024Causal Bandits for Linear Structural Equation Models
• ICML 2024A Sparsity Principle for Partially Observable Causal Representation Learning
• ICLR 2024Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
• CVPR 2024MuseChat: A Conversational Music Recommendation System for Videos
• NeurIPS 2023A Causal Framework for Decomposing Spurious Variations
• ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
• ICLR 2023Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
• CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
• NeurIPS 2026Bridging Image Restoration and Recognition via Causal Mediated Unrolling
• ICML 2026Causal Disentangled Anchor Learning for Scalable Fair Multi-view Clustering
• ICLR 2026Addressing divergent representations from causal interventions on neural networks
• CVPR 2026Bridge Basis-Driven Causal Inference Marries VFMs for Domain Generalization

全部因果推荐论文

共 43 篇，第 1 / 1 页

1. ICLR 2026ActiveCQ: Active Estimation of Causal Quantities
2. ICLR 2026Addressing divergent representations from causal interventions on neural networks
3. ICLR 2026Adjusting Prediction Model Through Wasserstein Geodesic for Causal Inference
4. ICLR 2026ALM-MTA: Front-Door Causal Multi-Touch Attribution Method for Creator-Ecosystem Optimization
5. ICLR 2026Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning
6. ICLR 2026Beyond Entity Correlations: Disentangling Event Causal Puzzles in Temporal Knowledge Graphs
7. ICLR 2026CARL: Preserving Causal Structure in Representation Learning
8. ICLR 2026Catalog-Native LLM: Speaking Item-ID dialect with Less Entanglement for Recommendation
9. ICLR 2026Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
10. ICLR 2026Causal Interpretation of Neural Network Computations with Contribution Decomposition (CODEC)
11. ICLR 2026Causal Score Conditioning for Multi-Resolution Latent Systems
12. ICLR 2026Causal-Steer: Disentangled Continuous Style Control without Parallel Corpora
13. ICLR 2026Causally Robust Preference Learning with Reasons
14. ICLR 2026Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
15. ICLR 2026Coarse-to-Fine Learning of Dynamic Causal Structures
16. ICLR 2026CollectiveKV: Decoupling and Sharing Collaborative Information in Sequential Recommendation
17. ICLR 2026Conditional Independent Component Analysis For Estimating Causal Structure with Latent Variables
18. ICLR 2026Contextual Causal Bayesian Optimisation
19. ICLR 2026Counterfactual Structural Causal Bandits
20. ICLR 2026Direct Doubly Robust Estimation of Conditional Quantile Contrasts
21. ICLR 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
22. ICLR 2026Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
23. ICLR 2026Exploratory Causal Inference in SAEnce
24. ICLR 2026Generalization of RLVR Using Causal Reasoning as a Testbed
25. ICLR 2026Heterogeneous Front-Door Effects: Debiased Estimation with Quasi-Oracle Guarantees
26. ICLR 2026How Transformers Learn Causal Structures In-Context: Explainable Mechanism Meets Theoretical Guarantee
27. ICLR 2026Learning Dynamic Causal Graphs Under Parametric Uncertainty via Polynomial Chaos Expansions
28. ICLR 2026Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies
29. ICLR 2026Low-pass Personalized Subgraph Federated Recommendation
30. ICLR 2026Matching without Group Barrier for Heterogeneous Treatment Effect Estimation
31. ICLR 2026More Than What Was Chosen: LLM-based Explainable Recommendation Beyond Noisy User Preferences
32. ICLR 2026On Measuring Influence in Avoiding Undesired Future
33. ICLR 2026On the identifiability of causal graphs with multiple environments
34. ICLR 2026Privacy-Protected Causal Survival Analysis Under Distribution Shift
35. ICLR 2026Query-Specific Causal Graph Pruning Under Tiered Knowledge
36. ICLR 2026Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
37. ICLR 2026Steering Diffusion Models Towards Credible Content Recommendation
38. ICLR 2026Stochastic Neural Networks for Causal Inference with Missing Confounders
39. ICLR 2026SurvHTE-Bench: A Benchmark for Heterogeneous Treatment Effect Estimation in Survival Analysis
40. ICLR 2026Testing Most Influential Sets
41. ICLR 2026Token-Efficient Long-Term Interest Sketching and Internalized Reasoning for LLM-based Recommendation
42. ICLR 2026Topological Causal Effects
43. ICLR 2026Truthful or Fabricated? Using Causal Attribution to Mitigate Reward Hacking in Explanations

常见问题

2023–2026 年四大会议一共有多少篇因果推荐论文？

共 548 篇，按年份分别是 2023 年 83 篇、2024 年 123 篇、2025 年 152 篇、2026 年 190 篇。

NeurIPS 2026 有多少篇因果推荐论文？

NeurIPS 2026 有 75 篇因果推荐论文，上一届是 69 篇。

ICLR 2026 有多少篇因果推荐论文？

ICLR 2026 有 43 篇因果推荐论文，上一届是 32 篇。

ICML 2026 有多少篇因果推荐论文？

ICML 2026 有 56 篇因果推荐论文，上一届是 49 篇。

CVPR 2026 有多少篇因果推荐论文？

CVPR 2026 有 16 篇因果推荐论文，上一届是 2 篇。

哪个会议的因果推荐论文最多？

NeurIPS，2023–2026 年共 227 篇。