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 152 篇，第 2 / 4 页

51. ICML 2025FairPFN: A Tabular Foundation Model for Causal Fairness
52. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
53. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
54. ICML 2025Generalized Random Forests Using Fixed-Point Trees
55. ICML 2025Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments
56. ICML 2025Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
57. ICML 2025Identifying biological perturbation targets through causal differential networks
58. ICML 2025Identifying Causal Direction via Variational Bayesian Compression
59. ICML 2025Integer Programming for Generalized Causal Bootstrap Designs
60. ICML 2025Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors
61. ICML 2025Isolated Causal Effects of Natural Language
62. ICML 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
63. ICML 2025Learning with Selectively Labeled Data from Multiple Decision-makers
64. ICML 2025LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
65. ICML 2025LLMScan: Causal Scan for LLM Misbehavior Detection
66. ICML 2025Local Identifying Causal Relations in the Presence of Latent Variables
67. ICML 2025Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
68. ICML 2025Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits
69. ICML 2025Multi-Objective Causal Bayesian Optimization
70. ICML 2025Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect
71. ICML 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
72. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
73. ICML 2025Preference Learning for AI Alignment: a Causal Perspective
74. ICML 2025Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies
75. ICML 2025RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals
76. ICML 2025Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
77. ICML 2025Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation
78. ICML 2025Teaching Transformers Causal Reasoning through Axiomatic Training
79. ICML 2025Telling Peer Direct Effects from Indirect Effects in Observational Network Data
80. ICML 2025Tightening Causal Bounds via Covariate-Aware Optimal Transport
81. ICML 2025Towards Robustness and Explainability of Automatic Algorithm Selection
82. ICML 2025Towards the Causal Complete Cause of Multi-Modal Representation Learning
83. ICML 2025Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning
84. NeurIPS 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
85. NeurIPS 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
86. NeurIPS 2025A Unified Framework for the Transportability of Population-Level Causal Measures
87. NeurIPS 2025An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
88. NeurIPS 2025BAM-ICL: Causal Hijacking In-Context Learning with Budgeted Adversarial Manipulation
89. NeurIPS 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliance
90. NeurIPS 2025Causal Climate Emulation with Bayesian Filtering
91. NeurIPS 2025Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
92. NeurIPS 2025Causal Explanation-Guided Learning for Organ Allocation
93. NeurIPS 2025Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers
94. NeurIPS 2025Causal LLM Routing: End-to-End Regret Minimization from Observational Data
95. NeurIPS 2025Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
96. NeurIPS 2025Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
97. NeurIPS 2025Causality Meets the Table: Debiasing LLMs for Faithful TableQA via Front-Door Intervention
98. NeurIPS 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
99. NeurIPS 2025CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
100. NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization

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。