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 75 篇，第 2 / 2 页

51. NeurIPS 2026Online Causal Configuration for Networked Systems via Doubly Robust Steady-State Learning
52. NeurIPS 2026Parasite Features: Causal Abstraction Through Spurious Pathways
53. NeurIPS 2026Position Without Positional Embeddings: A Directional Mechanism in NoPE Transformers
54. NeurIPS 2026PreDiff: Sequential Recommendation by Denoising Preference Distributions
55. NeurIPS 2026Proximal Difference-in-Differences for Long-Term Causal Learning under Confounding and Outcome Drift
56. NeurIPS 2026R-GRec: Relation-Guided Generative Recommendation via Collaborative Graph Supervision
57. NeurIPS 2026RecMem: Recurrent Memory Compression for Long-Sequence Recommendation
58. NeurIPS 2026Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models
59. NeurIPS 2026Rethinking Semantic ID Construction for Generative Recommendation: SimHash with Parallel Decoding and Semantic Alignment
60. NeurIPS 2026Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking
61. NeurIPS 2026Severity-Controlled Prediction Sets for Medication Recommendation
62. NeurIPS 2026Stable Partial Order Constraints for Temporal Causal Structure Learning
63. NeurIPS 2026Statistical Inference in Causal Partial Identification under Smooth Densities
64. NeurIPS 2026Structural Causal Bottleneck Models
65. NeurIPS 2026Towards a Universal Causal Reasoner
66. NeurIPS 2026TRACE: Tourism Recommendation with Accountable Citation Evidence
67. NeurIPS 2026TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
68. NeurIPS 2026Trie-Aware Transformers for Generative Recommendation
69. NeurIPS 2026Two-Clustering Regime of Token Dynamics in Causal Attention
70. NeurIPS 2026Validating Causal Abstraction Metrics on Simulated Complex Systems
71. NeurIPS 2026VAMIRec: Value-Aware Memory Intervention for Continual Recommendation
72. NeurIPS 2026What Gets Measured Gets Managed: Sign-aware Recommendation Needs Sign-aware Evaluation
73. NeurIPS 2026Who caused $Y$? Local identifiability for learning causal parents
74. NeurIPS 2026Why Are LLMs Confidently Wrong? Correcting Overconfident Errors via Causal Head Intervention
75. NeurIPS 2026xWhy: Causal Learning from 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。