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 123 篇，第 3 / 3 页

101. NeurIPS 2024Intervention and Conditioning in Causal Bayesian Networks
102. NeurIPS 2024Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
103. NeurIPS 2024Learning 1D Causal Visual Representation with De-focus Attention Networks
104. NeurIPS 2024Learning Identifiable Factorized Causal Representations of Cellular Responses
105. NeurIPS 2024Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
106. NeurIPS 2024Linear Causal Bandits: Unknown Graph and Soft Interventions
107. NeurIPS 2024Linear Causal Representation Learning from Unknown Multi-node Interventions
108. NeurIPS 2024Marginal Causal Flows for Validation and Inference
109. NeurIPS 2024Measuring Goal-Directedness
110. NeurIPS 2024Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
111. NeurIPS 2024Mitigating Object Hallucination via Concentric Causal Attention
112. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
113. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
114. NeurIPS 2024Proximal Causal Inference With Text Data
115. NeurIPS 2024PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
116. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
117. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
118. NeurIPS 2024Smoke and Mirrors in Causal Downstream Tasks
119. NeurIPS 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
120. NeurIPS 2024Towards Understanding Extrapolation: a Causal Lens
121. NeurIPS 2024Unified Covariate Adjustment for Causal Inference
122. NeurIPS 2024Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
123. NeurIPS 2024Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms

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。