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 84 篇，第 1 / 2 页

  1. NeurIPS 2024A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding
  2. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
  3. NeurIPS 2024Achievable distributional robustness when the robust risk is only partially identified
  4. NeurIPS 2024An effective framework for estimating individualized treatment rules
  5. NeurIPS 2024Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
  6. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
  7. NeurIPS 2024Causal Contrastive Learning for Counterfactual Regression Over Time
  8. NeurIPS 2024Causal Dependence Plots
  9. NeurIPS 2024Causal Discovery from Event Sequences by Local Cause-Effect Attribution
  10. NeurIPS 2024Causal discovery with endogenous context variables
  11. NeurIPS 2024Causal Effect Identification in a Sub-Population with Latent Variables
  12. NeurIPS 2024Causal Imitation for Markov Decision Processes: a Partial Identification Approach
  13. NeurIPS 2024Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects
  14. NeurIPS 2024Causal Temporal Representation Learning with Nonstationary Sparse Transition
  15. NeurIPS 2024Causal vs. Anticausal merging of predictors
  16. NeurIPS 2024Causal-learn: Causal Discovery in Python
  17. NeurIPS 2024Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
  18. NeurIPS 2024Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
  19. NeurIPS 2024Conditional Outcome Equivalence: A Quantile Alternative to CATE
  20. NeurIPS 2024Consistency of Neural Causal Partial Identification
  21. NeurIPS 2024Covariate Shift Corrected Conditional Randomization Test
  22. NeurIPS 2024DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation
  23. NeurIPS 2024Detecting and Measuring Confounding Using Causal Mechanism Shifts
  24. NeurIPS 2024Differentiable Structure Learning with Partial Orders
  25. NeurIPS 2024DiffPO: A causal diffusion model for learning distributions of potential outcomes
  26. NeurIPS 2024Discovery of the Hidden World with Large Language Models
  27. NeurIPS 2024Disentangled Representation Learning in Non-Markovian Causal Systems
  28. NeurIPS 2024Do causal predictors generalize better to new domains?
  29. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
  30. NeurIPS 2024Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments
  31. NeurIPS 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
  32. NeurIPS 2024Efficient Policy Evaluation Across Multiple Different Experimental Datasets
  33. NeurIPS 2024Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing
  34. NeurIPS 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
  35. NeurIPS 2024Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation
  36. NeurIPS 2024Fast Proxy Experiment Design for Causal Effect Identification
  37. NeurIPS 2024Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
  38. NeurIPS 2024From Causal to Concept-Based Representation Learning
  39. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
  40. NeurIPS 2024Hierarchical and Density-based Causal Clustering
  41. NeurIPS 2024Higher-Order Causal Message Passing for Experimentation with Complex Interference
  42. NeurIPS 2024Identifiability Analysis of Linear ODE Systems with Hidden Confounders
  43. NeurIPS 2024Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
  44. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
  45. NeurIPS 2024Identifying General Mechanism Shifts in Linear Causal Representations
  46. NeurIPS 2024Intervention and Conditioning in Causal Bayesian Networks
  47. NeurIPS 2024Interventional Causal Discovery in a Mixture of DAGs
  48. NeurIPS 2024Learning Discrete Concepts in Latent Hierarchical Models
  49. NeurIPS 2024Learning Discrete Latent Variable Structures with Tensor Rank Conditions
  50. NeurIPS 2024Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity

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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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