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 115 篇，第 2 / 3 页

51. ICLR 2025ContextGNN: Beyond Two-Tower Recommendation Systems
52. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
53. ICLR 2025Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark
54. ICLR 2025Doubly robust identification of treatment effects from multiple environments
55. ICLR 2025Efficient Causal Decision Making with One-sided Feedback
56. ICLR 2025Efficient Inference for Large Language Model-based Generative Recommendation
57. ICLR 2025Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
58. ICLR 2025From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy
59. ICLR 2025HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
60. ICLR 2025Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
61. ICLR 2025Interpretable Causal Representation Learning for Biological Data in the Pathway Space
62. ICLR 2025Language Representations Can be What Recommenders Need: Findings and Potentials
63. ICLR 2025Learning Causal Alignment for Reliable Disease Diagnosis
64. ICLR 2025Multimodal Quantitative Language for Generative Recommendation
65. ICLR 2025Neural Causal Graph for Interpretable and Intervenable Classification
66. ICLR 2025On the Identification of Temporal Causal Representation with Instantaneous Dependence
67. ICLR 2025RecFlow: An Industrial Full Flow Recommendation Dataset
68. ICLR 2025Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates
69. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
70. ICLR 2025Selective Induction Heads: How Transformers Select Causal Structures in Context
71. ICLR 2025SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
72. ICLR 2025Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
73. ICLR 2025Standardizing Structural Causal Models
74. ICLR 2025The Geometry of Categorical and Hierarchical Concepts in Large Language Models
75. ICLR 2025Unifying Causal Representation Learning with the Invariance Principle
76. ICLR 2024A Neural Framework for Generalized Causal Sensitivity Analysis
77. ICLR 2024Adversarial Causal Bayesian Optimization
78. ICLR 2024Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
79. ICLR 2024Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
80. ICLR 2024Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
81. ICLR 2024Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
82. ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
83. ICLR 2024Causally Aligned Curriculum Learning
84. ICLR 2024Conditional Instrumental Variable Regression with Representation Learning for Causal Inference
85. ICLR 2024Debiased Collaborative Filtering with Kernel-Based Causal Balancing
86. ICLR 2024Debiasing Algorithm through Model Adaptation
87. ICLR 2024Doubly Robust Proximal Causal Learning for Continuous Treatments
88. ICLR 2024Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
89. ICLR 2024Federated Recommendation with Additive Personalization
90. ICLR 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
91. ICLR 2024Identifiable Latent Polynomial Causal Models through the Lens of Change
92. ICLR 2024Learning in reverse causal strategic environments with ramifications on two sided markets
93. ICLR 2024Multi-View Causal Representation Learning with Partial Observability
94. ICLR 2024SpaCE: The Spatial Confounding Environment
95. ICLR 2024Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models
96. ICLR 2024Treatment Effects Estimation By Uniform Transformer
97. ICLR 2023Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
98. ICLR 2023Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
99. ICLR 2023Causal Confusion and Reward Misidentification in Preference-Based Reward Learning
100. ICLR 2023Causal Estimation for Text Data with (Apparent) Overlap Violations

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。