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 56 篇，第 1 / 2 页

1. ICML 2026A Causal Decomposition Approach for Fair Contextual Multi-Armed Bandits
2. ICML 2026A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
3. ICML 2026Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
4. ICML 2026AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation
5. ICML 2026Anti-causal domain generalization: Leveraging unlabeled data
6. ICML 2026Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture
7. ICML 2026Caracal: Causal Architecture via Spectral Mixing
8. ICML 2026Causal Attention with Lookahead Keys
9. ICML 2026Causal Direct Preference Optimization for Distributionally Robust Generative Recommendation
10. ICML 2026Causal Disentangled Anchor Learning for Scalable Fair Multi-view Clustering
11. ICML 2026Causal Effect Identifiability in the Presence of Latent Confounders Without Auxiliary Variables
12. ICML 2026Causal Feature Learning via Generalized Rayleigh Quotients
13. ICML 2026Causal Fine-Tuning under Latent Confounded Shift
14. ICML 2026Causal Flow Q-Learning for Robust Offline Reinforcement Learning
15. ICML 2026Causal Identification from Counterfactual Data: Completeness and Bounding Results
16. ICML 2026Causal Matrix Completion under Multiple Treatments via Mixed Synthetic Nearest Neighbors
17. ICML 2026Causal Preference Elicitation
18. ICML 2026Causal Representation Learning with Optimal Compression and Complex Treatments
19. ICML 2026Causal-EPIG: Causally Aligned Active CATE Estimation
20. ICML 2026Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
21. ICML 2026CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
22. ICML 2026Dissecting Causal Mechanism Shifts via FANS: Function And Noise Separation
23. ICML 2026Distributionally Robust Causal Abstractions
24. ICML 2026Do-Prompt: Causal Interventions Meet Variational Prompt Bottlenecks
25. ICML 2026Evaluating Bivariate Causal Statements Based on Mutual Compatibility
26. ICML 2026Factored Classifier-Free Guidance
27. ICML 2026Fast and Accurate Causal Parallel Decoding using Jacobi Forcing
28. ICML 2026Federated Causal Inference on Multi-Site Observational Data via Propensity Score Aggregation
29. ICML 2026Formalizing and Falsifying Causal Pathways of Rare Events
30. ICML 2026Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
31. ICML 2026Function-Valued Causal Influence in Nonlinear Time Series
32. ICML 2026Gateways to Tractability for Satisfiability in Pearl’s Causal Hierarchy
33. ICML 2026Gradient-Based Causal Tree Ensembles: A Backbone Architecture for Heterogeneous Treatment Effects
34. ICML 2026Hyperbolic RQ-VAE enhanced Generative Recommendation with Differential-Length Codebook Strategy
35. ICML 2026Identifying Partially Observed Causal Models from Heterogeneous/Nonstationary Data
36. ICML 2026Interventional Processes For Causal Uncertainty Quantification
37. ICML 2026Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement
38. ICML 2026Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?
39. ICML 2026Negative Sampling From the Ground Up: A Redesign for Recommendation
40. ICML 2026On the Identifiability of Poisson Branching Structural Causal Model Under Latent Confounding
41. ICML 2026Relational Structural Causal Models
42. ICML 2026Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
43. ICML 2026Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
44. ICML 2026Spiked-CFR: Causal Representation Learning from LLMs via Wasserstein Projection Pursuit
45. ICML 2026Stem: Rethinking Causal Information Flow in Sparse Attention
46. ICML 2026Streaming Covariate Balancing via Discrepancy-Based Feature Coresets
47. ICML 2026SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
48. ICML 2026Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
49. ICML 2026The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
50. ICML 2026The Fairness Hierarchy: A viewpoint from causal inference

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。