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 69 篇，第 1 / 2 页

1. NeurIPS 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
2. NeurIPS 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
3. NeurIPS 2025A Unified Framework for the Transportability of Population-Level Causal Measures
4. NeurIPS 2025An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
5. NeurIPS 2025BAM-ICL: Causal Hijacking In-Context Learning with Budgeted Adversarial Manipulation
6. NeurIPS 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliance
7. NeurIPS 2025Causal Climate Emulation with Bayesian Filtering
8. NeurIPS 2025Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
9. NeurIPS 2025Causal Explanation-Guided Learning for Organ Allocation
10. NeurIPS 2025Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers
11. NeurIPS 2025Causal LLM Routing: End-to-End Regret Minimization from Observational Data
12. NeurIPS 2025Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
13. NeurIPS 2025Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
14. NeurIPS 2025Causality Meets the Table: Debiasing LLMs for Faithful TableQA via Front-Door Intervention
15. NeurIPS 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
16. NeurIPS 2025CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
17. NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
18. NeurIPS 2025CMoB: Modality Valuation via Causal Effect for Balanced Multimodal Learning
19. NeurIPS 2025Conformal Prediction for Causal Effects of Continuous Treatments
20. NeurIPS 2025Confounding Robust Deep Reinforcement Learning: A Causal Approach
21. NeurIPS 2025Curious Causality-Seeking Agents Learn Meta Causal World
22. NeurIPS 2025CVGL: Causal Learning and Geometric Topology
23. NeurIPS 2025Data Fusion for Partial Identification of Causal Effects
24. NeurIPS 2025DeCaFlow: A deconfounding causal generative model
25. NeurIPS 2025Decoding Causal Structure: End-to-End Mediation Pathways Inference
26. NeurIPS 2025Density Ratio-Free Doubly Robust Proxy Causal Learning
27. NeurIPS 2025Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
28. NeurIPS 2025Distributed mediation analysis with communication efficiency
29. NeurIPS 2025Do-PFN: In-Context Learning for Causal Effect Estimation
30. NeurIPS 2025Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem
31. NeurIPS 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
32. NeurIPS 2025Estimation of Treatment Effects in Extreme and Unobserved Data
33. NeurIPS 2025Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
34. NeurIPS 2025Fast exact recovery of noisy matrix from few entries: the infinity norm approach
35. NeurIPS 2025Faster Generic Identification in Tree-Shaped Structural Causal Models
36. NeurIPS 2025From Black-box to Causal-box: Towards Building More Interpretable Models
37. NeurIPS 2025Graph-Theoretic Insights into Bayesian Personalized Ranking for Recommendation
38. NeurIPS 2025HPSERec: A Hierarchical Partitioning and Stepwise Enhancement Framework for Long-tailed Sequential Recommendation
39. NeurIPS 2025Identifying Macro Causal Effects in C-DMGs over DMGs
40. NeurIPS 2025It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
41. NeurIPS 2025Make Information Diffusion Explainable: LLM-based Causal Framework for Diffusion Prediction
42. NeurIPS 2025Measure-Theoretic Anti-Causal Representation Learning
43. NeurIPS 2025Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
44. NeurIPS 2025Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
45. NeurIPS 2025Neuro-Spectral Architectures for Causal Physics-Informed Networks
46. NeurIPS 2025Non-Markovian Discrete Diffusion with Causal Language Models
47. NeurIPS 2025Non-Stationary Structural Causal Bandits
48. NeurIPS 2025ORBIT - Open Recommendation Benchmark for Reproducible Research with Hidden Tests
49. NeurIPS 2025P-Law: Predicting Quantitative Scaling Law with Entropy Guidance in Large Recommendation Models
50. NeurIPS 2025Path-Enhanced Contrastive Learning for Recommendation

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。