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 5 / 7 页

  1. NeurIPS 2024Targeted Sequential Indirect Experiment Design
  2. NeurIPS 2024Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding
  3. NeurIPS 2024Towards Understanding Extrapolation: a Causal Lens
  4. NeurIPS 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learning
  5. NeurIPS 2024Unified Covariate Adjustment for Causal Inference
  6. NeurIPS 2024Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
  7. NeurIPS 2024Using Surrogates in Covariate-adjusted Response-adaptive Randomization Experiments with Delayed Outcomes
  8. NeurIPS 2024When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding
  9. CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
  10. CVPR 2023Meta-Causal Learning for Single Domain Generalization
  11. ICLR 2023Causal Balancing for Domain Generalization
  12. ICLR 2023Combinatorial Pure Exploration of Causal Bandits
  13. ICLR 2023Estimating individual treatment effects under unobserved confounding using binary instruments
  14. ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
  15. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
  16. ICLR 2023Learning to Induce Causal Structure
  17. ICML 2023Active causal structure learning with advice
  18. ICML 2023Active Learning based Structural Inference
  19. ICML 2023Approximate Causal Effect Identification under Weak Confounding
  20. ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
  21. ICML 2023Causal Bounds in Quasi-Markovian Graphs
  22. ICML 2023Causal Isotonic Calibration for Heterogeneous Treatment Effects
  23. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
  24. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
  25. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
  26. ICML 2023Collaborative Causal Inference with Fair Incentives
  27. ICML 2023Conditions and Assumptions for Constraint-based Causal Structure Learning
  28. ICML 2023Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
  29. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
  30. ICML 2023Covariate balancing using the integral probability metric for causal inference
  31. ICML 2023Estimating Causal Effects using a Multi-task Deep Ensemble
  32. ICML 2023Estimating Possible Causal Effects with Latent Variables via Adjustment
  33. ICML 2023Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space
  34. ICML 2023Linear Causal Disentanglement via Interventions
  35. ICML 2023Neural Algorithmic Reasoning with Causal Regularisation
  36. ICML 2023On Data Manifolds Entailed by Structural Causal Models
  37. ICML 2023On the Relationship Between Explanation and Prediction: A Causal View
  38. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
  39. ICML 2023Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
  40. ICML 2023Towards Trustworthy Explanation: On Causal Rationalization
  41. ICML 2023Which Invariance Should We Transfer? A Causal Minimax Learning Approach
  42. NeurIPS 2023A Causal Framework for Decomposing Spurious Variations
  43. NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
  44. NeurIPS 2023A Measure-Theoretic Axiomatisation of Causality
  45. NeurIPS 2023A Unified Detection Framework for Inference-Stage Backdoor Defenses
  46. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
  47. NeurIPS 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effect
  48. NeurIPS 2023Analysis of Variance of Multiple Causal Networks
  49. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
  50. NeurIPS 2023ARTree: A Deep Autoregressive Model for Phylogenetic Inference

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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"}

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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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