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

每年论文数

各会议论文数

按年份和会议统计

因果推荐论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023261931783
20245721396123
20256932492152
202675435616190

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

全部因果推荐论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 227 篇，第 4 / 5 页

  1. NeurIPS 2024Causal Contrastive Learning for Counterfactual Regression Over Time
  2. NeurIPS 2024Causal Dependence Plots
  3. NeurIPS 2024Causal Effect Identification in a Sub-Population with Latent Variables
  4. NeurIPS 2024Causal Imitation for Markov Decision Processes: a Partial Identification Approach
  5. NeurIPS 2024Causal Temporal Representation Learning with Nonstationary Sparse Transition
  6. NeurIPS 2024Causal vs. Anticausal merging of predictors
  7. NeurIPS 2024Clustering in Causal Attention Masking
  8. NeurIPS 2024Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
  9. NeurIPS 2024Consistency of Neural Causal Partial Identification
  10. NeurIPS 2024CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
  11. NeurIPS 2024Detecting and Measuring Confounding Using Causal Mechanism Shifts
  12. NeurIPS 2024Disentangled Representation Learning in Non-Markovian Causal Systems
  13. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
  14. NeurIPS 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Data
  15. NeurIPS 2024End-to-end Learnable Clustering for Intent Learning in Recommendation
  16. NeurIPS 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
  17. NeurIPS 2024Fast Proxy Experiment Design for Causal Effect Identification
  18. NeurIPS 2024Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination
  19. NeurIPS 2024Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
  20. NeurIPS 2024From Causal to Concept-Based Representation Learning
  21. NeurIPS 2024Hierarchical and Density-based Causal Clustering
  22. NeurIPS 2024Higher-Order Causal Message Passing for Experimentation with Complex Interference
  23. NeurIPS 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
  24. NeurIPS 2024Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
  25. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
  26. NeurIPS 2024Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
  27. NeurIPS 2024In Pursuit of Causal Label Correlations for Multi-label Image Recognition
  28. NeurIPS 2024IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
  29. NeurIPS 2024Intervention and Conditioning in Causal Bayesian Networks
  30. NeurIPS 2024Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
  31. NeurIPS 2024Learning 1D Causal Visual Representation with De-focus Attention Networks
  32. NeurIPS 2024Learning Identifiable Factorized Causal Representations of Cellular Responses
  33. NeurIPS 2024Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
  34. NeurIPS 2024Linear Causal Bandits: Unknown Graph and Soft Interventions
  35. NeurIPS 2024Linear Causal Representation Learning from Unknown Multi-node Interventions
  36. NeurIPS 2024Marginal Causal Flows for Validation and Inference
  37. NeurIPS 2024Measuring Goal-Directedness
  38. NeurIPS 2024Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
  39. NeurIPS 2024Mitigating Object Hallucination via Concentric Causal Attention
  40. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
  41. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
  42. NeurIPS 2024Proximal Causal Inference With Text Data
  43. NeurIPS 2024PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
  44. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
  45. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
  46. NeurIPS 2024Smoke and Mirrors in Causal Downstream Tasks
  47. NeurIPS 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
  48. NeurIPS 2024Towards Understanding Extrapolation: a Causal Lens
  49. NeurIPS 2024Unified Covariate Adjustment for Causal Inference
  50. NeurIPS 2024Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?

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"}

两分钟接入 Paperlist MCP（Claude Code、Cursor、Codex、ChatGPT）

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。

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