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 227 篇，第 3 / 5 页

101. NeurIPS 2025Density Ratio-Free Doubly Robust Proxy Causal Learning
102. NeurIPS 2025Disentangling misreporting from genuine adaptation in strategic settings: a causal approach
103. NeurIPS 2025Distributed mediation analysis with communication efficiency
104. NeurIPS 2025Do-PFN: In-Context Learning for Causal Effect Estimation
105. NeurIPS 2025Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem
106. NeurIPS 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
107. NeurIPS 2025Estimation of Treatment Effects in Extreme and Unobserved Data
108. NeurIPS 2025Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
109. NeurIPS 2025Fast exact recovery of noisy matrix from few entries: the infinity norm approach
110. NeurIPS 2025Faster Generic Identification in Tree-Shaped Structural Causal Models
111. NeurIPS 2025From Black-box to Causal-box: Towards Building More Interpretable Models
112. NeurIPS 2025Graph-Theoretic Insights into Bayesian Personalized Ranking for Recommendation
113. NeurIPS 2025HPSERec: A Hierarchical Partitioning and Stepwise Enhancement Framework for Long-tailed Sequential Recommendation
114. NeurIPS 2025Identifying Macro Causal Effects in C-DMGs over DMGs
115. NeurIPS 2025It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
116. NeurIPS 2025Make Information Diffusion Explainable: LLM-based Causal Framework for Diffusion Prediction
117. NeurIPS 2025Measure-Theoretic Anti-Causal Representation Learning
118. NeurIPS 2025Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
119. NeurIPS 2025Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
120. NeurIPS 2025Neuro-Spectral Architectures for Causal Physics-Informed Networks
121. NeurIPS 2025Non-Markovian Discrete Diffusion with Causal Language Models
122. NeurIPS 2025Non-Stationary Structural Causal Bandits
123. NeurIPS 2025ORBIT - Open Recommendation Benchmark for Reproducible Research with Hidden Tests
124. NeurIPS 2025P-Law: Predicting Quantitative Scaling Law with Entropy Guidance in Large Recommendation Models
125. NeurIPS 2025Path-Enhanced Contrastive Learning for Recommendation
126. NeurIPS 2025Photography Perspective Composition: Towards Aesthetic Perspective Recommendation
127. NeurIPS 2025Pinpointing Attention-Causal Communication in Language Models
128. NeurIPS 2025Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference
129. NeurIPS 2025Prediction-Powered Causal Inferences
130. NeurIPS 2025PUATE: Efficient ATE Estimation from Treated (Positive) and Unlabeled Units
131. NeurIPS 2025Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
132. NeurIPS 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
133. NeurIPS 2025Reward-oriented Causal Representation Learning
134. NeurIPS 2025SPARTAN: A Sparse Transformer World Model Attending to What Matters
135. NeurIPS 2025SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
136. NeurIPS 2025Think before Recommendation: Autonomous Reasoning-enhanced Recommender
137. NeurIPS 2025Towards Identifiability of Hierarchical Temporal Causal Representation Learning
138. NeurIPS 2025Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts
139. NeurIPS 2025Transferring Causal Effects using Proxies
140. NeurIPS 2025Tree of Preferences for Diversified Recommendation
141. NeurIPS 2025Turning Sand to Gold: Recycling Data to Bridge On-Policy and Off-Policy Learning via Causal Bound
142. NeurIPS 2025TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
143. NeurIPS 2025Universal Causal Inference in a Topos
144. NeurIPS 2025When Causal Dynamics Matter: Adapting Causal Strategies through Meta-Aware Interventions
145. NeurIPS 2024A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
146. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
147. NeurIPS 2024Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
148. NeurIPS 2024Analysing the Generalisation and Reliability of Steering Vectors
149. NeurIPS 2024BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
150. NeurIPS 2024Causal Bandits for Linear Structural Equation Models

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。