深度学习 · causal inference论文趋势 2023–2026

主要结论

• 深度学习 · causal inference论文从 2023 年的 108 篇降到 2026 年的 66 篇，热度在回落。
• NeurIPS 是发深度学习 · causal inference论文最多的会议，2023–2026 年共 196 篇，占四个会议总量的 62%。
• 交叉方向里涨得最快的是因果推荐：同时属于两个主题的论文从 2023 年的 27 篇增至 2026 年的 55 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 10；同组增长最快的扩散模型是 4.1 倍，深度学习 · causal inference是 0.6 倍。

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

• NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
• ICML 2026Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
• ICLR 2026Causal-Steer: Disentangled Continuous Style Control without Parallel Corpora
• CVPR 2026Breaking Spurious Correlations Uncertainty-Driven Causal Transformers for AU Detection
• NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
• ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
• ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
• CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
• NeurIPS 2024A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding
• ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
• ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
• CVPR 2024Bi-Causal: Group Activity Recognition via Bidirectional Causality
• NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
• ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
• ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
• CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
• NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
• ICML 2026Caracal: Causal Architecture via Spectral Mixing
• ICLR 2026Counterfactual Structural Causal Bandits
• CVPR 2026Bridge Basis-Driven Causal Inference Marries VFMs for Domain Generalization

全部深度学习 · causal inference论文

共 108 篇，第 2 / 3 页

51. NeurIPS 2024Disentangled Representation Learning in Non-Markovian Causal Systems
52. NeurIPS 2024Do causal predictors generalize better to new domains?
53. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
54. NeurIPS 2024Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments
55. NeurIPS 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
56. NeurIPS 2024Efficient Policy Evaluation Across Multiple Different Experimental Datasets
57. NeurIPS 2024Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing
58. NeurIPS 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
59. NeurIPS 2024Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation
60. NeurIPS 2024Fast Proxy Experiment Design for Causal Effect Identification
61. NeurIPS 2024Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
62. NeurIPS 2024From Causal to Concept-Based Representation Learning
63. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
64. NeurIPS 2024Hierarchical and Density-based Causal Clustering
65. NeurIPS 2024Higher-Order Causal Message Passing for Experimentation with Complex Interference
66. NeurIPS 2024Identifiability Analysis of Linear ODE Systems with Hidden Confounders
67. NeurIPS 2024Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
68. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
69. NeurIPS 2024Identifying General Mechanism Shifts in Linear Causal Representations
70. NeurIPS 2024Intervention and Conditioning in Causal Bayesian Networks
71. NeurIPS 2024Interventional Causal Discovery in a Mixture of DAGs
72. NeurIPS 2024Learning Discrete Concepts in Latent Hierarchical Models
73. NeurIPS 2024Learning Discrete Latent Variable Structures with Tensor Rank Conditions
74. NeurIPS 2024Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
75. NeurIPS 2024Learning Mixtures of Unknown Causal Interventions
76. NeurIPS 2024Learning the Optimal Policy for Balancing Short-Term and Long-Term Rewards
77. NeurIPS 2024Linear Causal Representation Learning from Unknown Multi-node Interventions
78. NeurIPS 2024Marginal Causal Flows for Validation and Inference
79. NeurIPS 2024Markov Equivalence and Consistency in Differentiable Structure Learning
80. NeurIPS 2024Marrying Causal Representation Learning with Dynamical Systems for Science
81. NeurIPS 2024Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
82. NeurIPS 2024Natural Counterfactuals With Necessary Backtracking
83. NeurIPS 2024Off-policy estimation with adaptively collected data: the power of online learning
84. NeurIPS 2024On Causal Discovery in the Presence of Deterministic Relations
85. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
86. NeurIPS 2024On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function
87. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
88. NeurIPS 2024Optimization-based Causal Estimation from Heterogeneous Environments
89. NeurIPS 2024Ordering-Based Causal Discovery for Linear and Nonlinear Relations
90. NeurIPS 2024Partial Transportability for Domain Generalization
91. NeurIPS 2024Proximal Causal Inference With Text Data
92. NeurIPS 2024Qualitative Mechanism Independence
93. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
94. NeurIPS 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
95. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
96. NeurIPS 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventions
97. NeurIPS 2024Smoke and Mirrors in Causal Downstream Tasks
98. NeurIPS 2024Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
99. NeurIPS 2024Structured Learning of Compositional Sequential Interventions
100. NeurIPS 2024Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems

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。