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 1 / 5 页

  1. NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
  2. NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
  3. NeurIPS 2026ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
  4. NeurIPS 2026AdaM-Rec: Adaptive Modality Routing for Multimodal Recommendation
  5. NeurIPS 2026AdaSRU: Adaptive Source-Free Recommendation Unlearning via Gradient-Constrained Optimization
  6. NeurIPS 2026An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
  7. NeurIPS 2026Artificial Aphasias in Lesioned Language Models
  8. NeurIPS 2026Automated Causal Effect Estimation through Self-Evolving AI
  9. NeurIPS 2026Bayesian Causal Stress Testing: Posterior Fragility of Treatment-Effect Conclusions
  10. NeurIPS 2026Bridging Image Restoration and Recognition via Causal Mediated Unrolling
  11. NeurIPS 2026CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series
  12. NeurIPS 2026Causal Abstractions, Categorically Unified
  13. NeurIPS 2026Causal Bias Detection in Generative Artifical Intelligence
  14. NeurIPS 2026Causal Effect Identification with a Single Agnostic Proxy
  15. NeurIPS 2026Causal Gradient Steering: Exposing Shortcut Gradients by Destroying Causal Signal
  16. NeurIPS 2026Causal Inference for Sequential Settings under Interference and Latent Confounding
  17. NeurIPS 2026Causal learning with the invariance principle
  18. NeurIPS 2026Causal Multi-Task Demand Learning
  19. NeurIPS 2026Causal pieces: analysing and improving spiking neural networks piece by piece
  20. NeurIPS 2026Causal Representation Learning for Generalisable Recommendation
  21. NeurIPS 2026Causal Survival Forests with Negative Controls
  22. NeurIPS 2026Causal-VLM: Dense Causal Captioning in Videos
  23. NeurIPS 2026Characterizing Underrepresentation in Generalizing Causal Survival Estimates
  24. NeurIPS 2026Coarsening Linear Non-Gaussian Causal Models with Cycles
  25. NeurIPS 2026Confounding-Aware Client Selection in Federated Learning via Causal Mediation Analysis
  26. NeurIPS 2026CorridorLight: Cooperation as Task Negotiation with Causal Gating for Traffic Signal Control
  27. NeurIPS 2026Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
  28. NeurIPS 2026Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation
  29. NeurIPS 2026Decision Path Tracing for Causal Analysis in Transformers
  30. NeurIPS 2026Decomposing Effects in Neural Causal Models
  31. NeurIPS 2026Detect What You Need: Chain-of-Causal Reasoning for 3D Intent Grounding
  32. NeurIPS 2026DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
  33. NeurIPS 2026Emergent representations of graphical structure in mechanistic neural models of causal judgment
  34. NeurIPS 2026From Document Layout to Causal Topology: An End-to-End Architecture for Temporal Causal Reasoning
  35. NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
  36. NeurIPS 2026Full-Sequence Masked Diffusion for Generative Recommendation
  37. NeurIPS 2026Generalized Bayes for Causal Inference
  38. NeurIPS 2026HARP: Training-Free Dual-Profile Agentic Communication for LLM-Based Recommendation
  39. NeurIPS 2026Identifying Structural Biases from Causal Mechanism Shifts
  40. NeurIPS 2026Improving Causal Explanations
  41. NeurIPS 2026IntentLens: Grounding Underspecified Multimodal Queries for Recommendation via Tool-Augmented Reasoning
  42. NeurIPS 2026Is Your LLM-as-a-Recommender Agent Trustable? LLMs' Recommendation is Easily Hacked by Biases (Preferences)
  43. NeurIPS 2026Learning Contextual Causal Dynamics for Robust Exploration in Reinforcement Learning
  44. NeurIPS 2026Learning Deployable Causal Action Geometry under Temporal Non-Stationarity
  45. NeurIPS 2026LLM Routing Through the Lens of Recommendation: A Roadmap for Efficient AI Orchestration
  46. NeurIPS 2026MARS: Multi-resolution Adaptive Routing for Sequential Recommendation
  47. NeurIPS 2026MOCHA: Discovering Multi-Order Dynamic Causal Structure in Temporal Point Processes
  48. NeurIPS 2026Multi-Objective Causal Bandits: Minimal Intervention Space and Policy-Level Learning
  49. NeurIPS 2026Natural Jammers: When Non-Robust Features Become Antagonistic
  50. NeurIPS 2026Neural Causal Models under Markov Equivalence

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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