深度学习 · causal inference论文趋势 2023–2026

ICML/NeurIPS 深度学习方向：causal_inference

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 316 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

深度学习 · causal inference论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
2023756252108
2024846171108
20259618134
202628826466

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

全部深度学习 · causal inference论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 316 篇，第 2 / 7 页

  1. NeurIPS 2026DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
  2. NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
  3. NeurIPS 2026Generalized Bayes for Causal Inference
  4. NeurIPS 2026Improving Causal Explanations
  5. NeurIPS 2026Learning Contextual Causal Dynamics for Robust Exploration in Reinforcement Learning
  6. NeurIPS 2026Learning Deployable Causal Action Geometry under Temporal Non-Stationarity
  7. NeurIPS 2026Neural Causal Models under Markov Equivalence
  8. NeurIPS 2026Online Causal Configuration for Networked Systems via Doubly Robust Steady-State Learning
  9. NeurIPS 2026Revealing Epistemic Uncertainty in MLLMs via Causal-Invariant Masking
  10. NeurIPS 2026Statistical Inference in Causal Partial Identification under Smooth Densities
  11. NeurIPS 2026Structural Causal Bottleneck Models
  12. NeurIPS 2026Towards a Universal Causal Reasoner
  13. NeurIPS 2026Two-Clustering Regime of Token Dynamics in Causal Attention
  14. NeurIPS 2026Who caused $Y$? Local identifiability for learning causal parents
  15. NeurIPS 2026Why Are LLMs Confidently Wrong? Correcting Overconfident Errors via Causal Head Intervention
  16. NeurIPS 2026xWhy: Causal Learning from Explanations
  17. CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
  18. ICLR 2025A Robust Method to Discover Causal or Anticausal Relation
  19. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
  20. ICLR 2025Doubly robust identification of treatment effects from multiple environments
  21. ICLR 2025Incremental Causal Effect for Time to Treatment Initialization
  22. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
  23. ICLR 2025Standardizing Structural Causal Models
  24. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
  25. ICML 2025Causal Abstraction Inference under Lossy Representations
  26. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
  27. ICML 2025Causal Attribution Analysis for Continuous Outcomes
  28. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
  29. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
  30. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
  31. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
  32. ICML 2025Generalized Random Forests Using Fixed-Point Trees
  33. ICML 2025Integer Programming for Generalized Causal Bootstrap Designs
  34. ICML 2025Isolated Causal Effects of Natural Language
  35. ICML 2025Learning with Selectively Labeled Data from Multiple Decision-makers
  36. ICML 2025LLMScan: Causal Scan for LLM Misbehavior Detection
  37. ICML 2025Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
  38. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
  39. ICML 2025Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
  40. ICML 2025Telling Peer Direct Effects from Indirect Effects in Observational Network Data
  41. ICML 2025Tightening Causal Bounds via Covariate-Aware Optimal Transport
  42. NeurIPS 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
  43. NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
  44. NeurIPS 2025Data Fusion for Partial Identification of Causal Effects
  45. NeurIPS 2025Decoding Causal Structure: End-to-End Mediation Pathways Inference
  46. NeurIPS 2025Density Ratio-Free Doubly Robust Proxy Causal Learning
  47. NeurIPS 2025Do-PFN: In-Context Learning for Causal Effect Estimation
  48. NeurIPS 2025Prediction-Powered Causal Inferences
  49. NeurIPS 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
  50. NeurIPS 2025Structural Causal Bandits 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 查深度学习 · causal inference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": "dl-causal-inference"}

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深度学习 · causal inference论文？
共 316 篇，按年份分别是 2023 年 108 篇、2024 年 108 篇、2025 年 34 篇、2026 年 66 篇。
NeurIPS 2026 有多少篇深度学习 · causal inference论文？
NeurIPS 2026 有 28 篇深度学习 · causal inference论文，上一届是 9 篇。
ICLR 2026 有多少篇深度学习 · causal inference论文？
ICLR 2026 有 8 篇深度学习 · causal inference论文，上一届是 6 篇。
ICML 2026 有多少篇深度学习 · causal inference论文？
ICML 2026 有 26 篇深度学习 · causal inference论文，上一届是 18 篇。
CVPR 2026 有多少篇深度学习 · causal inference论文？
CVPR 2026 有 4 篇深度学习 · causal inference论文，上一届是 1 篇。
哪个会议的深度学习 · causal inference论文最多？
NeurIPS，2023–2026 年共 196 篇。

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