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 49 篇，第 1 / 1 页

1. ICML 2025A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
2. ICML 2025A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
3. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
4. ICML 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
5. ICML 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
6. ICML 2025Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?
7. ICML 2025Causal Abstraction Inference under Lossy Representations
8. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
9. ICML 2025Causal Attribution Analysis for Continuous Outcomes
10. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
11. ICML 2025Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning
12. ICML 2025Causal Logistic Bandits with Counterfactual Fairness Constraints
13. ICML 2025Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models
14. ICML 2025Distinguishing Cause from Effect with Causal Velocity Models
15. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
16. ICML 2025ENSUR: Equitable and Statistically Unbiased Recommendation
17. ICML 2025FairPFN: A Tabular Foundation Model for Causal Fairness
18. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
19. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
20. ICML 2025Generalized Random Forests Using Fixed-Point Trees
21. ICML 2025Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments
22. ICML 2025Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
23. ICML 2025Identifying biological perturbation targets through causal differential networks
24. ICML 2025Identifying Causal Direction via Variational Bayesian Compression
25. ICML 2025Integer Programming for Generalized Causal Bootstrap Designs
26. ICML 2025Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors
27. ICML 2025Isolated Causal Effects of Natural Language
28. ICML 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
29. ICML 2025Learning with Selectively Labeled Data from Multiple Decision-makers
30. ICML 2025LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
31. ICML 2025LLMScan: Causal Scan for LLM Misbehavior Detection
32. ICML 2025Local Identifying Causal Relations in the Presence of Latent Variables
33. ICML 2025Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
34. ICML 2025Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits
35. ICML 2025Multi-Objective Causal Bayesian Optimization
36. ICML 2025Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect
37. ICML 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
38. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
39. ICML 2025Preference Learning for AI Alignment: a Causal Perspective
40. ICML 2025Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies
41. ICML 2025RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals
42. ICML 2025Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
43. ICML 2025Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation
44. ICML 2025Teaching Transformers Causal Reasoning through Axiomatic Training
45. ICML 2025Telling Peer Direct Effects from Indirect Effects in Observational Network Data
46. ICML 2025Tightening Causal Bounds via Covariate-Aware Optimal Transport
47. ICML 2025Towards Robustness and Explainability of Automatic Algorithm Selection
48. ICML 2025Towards the Causal Complete Cause of Multi-Modal Representation Learning
49. ICML 2025Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning

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。