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 3 / 4 页

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

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。