因果推荐论文趋势 2023–2026
去偏与 uplift
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 548 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 26 | 19 | 31 | 7 | 83 |
| 2024 | 57 | 21 | 39 | 6 | 123 |
| 2025 | 69 | 32 | 49 | 2 | 152 |
| 2026 | 75 | 43 | 56 | 16 | 190 |
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 175 篇，第 4 / 4 页
- ICML 2023Causal Strategic Classification: A Tale of Two Shifts
- ICML 2023Collaborative Causal Inference with Fair Incentives
- ICML 2023Constrained Causal Bayesian Optimization
- ICML 2023Covariate balancing using the integral probability metric for causal inference
- ICML 2023Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation
- ICML 2023Differentiable Multi-Target Causal Bayesian Experimental Design
- ICML 2023DRCFS: Doubly Robust Causal Feature Selection
- ICML 2023Estimating Possible Causal Effects with Latent Variables via Adjustment
- ICML 2023Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
- ICML 2023How Bad is Top-$K$ Recommendation under Competing Content Creators?
- ICML 2023Instrumental Variable Estimation of Average Partial Causal Effects
- ICML 2023Interventional Causal Representation Learning
- ICML 2023Linear Causal Disentanglement via Interventions
- ICML 2023Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
- ICML 2023Neural Algorithmic Reasoning with Causal Regularisation
- ICML 2023New metrics and search algorithms for weighted causal DAGs
- ICML 2023On Data Manifolds Entailed by Structural Causal Models
- ICML 2023On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
- ICML 2023On the Identifiability and Estimation of Causal Location-Scale Noise Models
- ICML 2023Performative Recommendation: Diversifying Content via Strategic Incentives
- ICML 2023Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
- ICML 2023Propensity Matters: Measuring and Enhancing Balancing for Recommendation
- ICML 2023Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
- ICML 2023Towards Trustworthy Explanation: On Causal Rationalization
- ICML 2023Which Invariance Should We Transfer? A Causal Minimax Learning Approach
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查因果推荐在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "causal-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。