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 2 / 3 页

51. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
52. ICML 2024Multiply-Robust Causal Change Attribution
53. ICML 2024On Positivity Condition for Causal Inference
54. ICML 2024On the Embedding Collapse when Scaling up Recommendation Models
55. ICML 2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
56. ICML 2024On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation
57. ICML 2024Position: The Causal Revolution Needs Scientific Pragmatism
58. ICML 2024Prediction-powered Generalization of Causal Inferences
59. ICML 2024Reducing Balancing Error for Causal Inference via Optimal Transport
60. ICML 2024StableMask: Refining Causal Masking in Decoder-only Transformer
61. ICML 2024Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
62. ICML 2024Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
63. ICML 2024Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
64. ICML 2024Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints
65. ICML 2024Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias
66. ICML 2024Wukong: Towards a Scaling Law for Large-Scale Recommendation
67. NeurIPS 2024A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
68. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
69. NeurIPS 2024Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
70. NeurIPS 2024Analysing the Generalisation and Reliability of Steering Vectors
71. NeurIPS 2024BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
72. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
73. NeurIPS 2024Causal Contrastive Learning for Counterfactual Regression Over Time
74. NeurIPS 2024Causal Dependence Plots
75. NeurIPS 2024Causal Effect Identification in a Sub-Population with Latent Variables
76. NeurIPS 2024Causal Imitation for Markov Decision Processes: a Partial Identification Approach
77. NeurIPS 2024Causal Temporal Representation Learning with Nonstationary Sparse Transition
78. NeurIPS 2024Causal vs. Anticausal merging of predictors
79. NeurIPS 2024Clustering in Causal Attention Masking
80. NeurIPS 2024Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
81. NeurIPS 2024Consistency of Neural Causal Partial Identification
82. NeurIPS 2024CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
83. NeurIPS 2024Detecting and Measuring Confounding Using Causal Mechanism Shifts
84. NeurIPS 2024Disentangled Representation Learning in Non-Markovian Causal Systems
85. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
86. NeurIPS 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Data
87. NeurIPS 2024End-to-end Learnable Clustering for Intent Learning in Recommendation
88. NeurIPS 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
89. NeurIPS 2024Fast Proxy Experiment Design for Causal Effect Identification
90. NeurIPS 2024Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination
91. NeurIPS 2024Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
92. NeurIPS 2024From Causal to Concept-Based Representation Learning
93. NeurIPS 2024Hierarchical and Density-based Causal Clustering
94. NeurIPS 2024Higher-Order Causal Message Passing for Experimentation with Complex Interference
95. NeurIPS 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
96. NeurIPS 2024Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
97. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
98. NeurIPS 2024Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
99. NeurIPS 2024In Pursuit of Causal Label Correlations for Multi-label Image Recognition
100. NeurIPS 2024IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark

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。