因果发现论文趋势 2023–2026

主要结论

• 因果发现论文从 2023 年的 59 篇增长到 2026 年的 139 篇，三年间扩大到约 2.4 倍。
• NeurIPS 是发因果发现论文最多的会议，2023–2026 年共 150 篇，占四个会议总量的 40%。
• 交叉方向里涨得最快的是通用机器学习 · unsupervised and semisupervised learning：同时属于两个主题的论文从 2023 年的 3 篇增至 2026 年的 10 篇。
• 在「因果与公平」的 5 个主题里，它的 2026 年论文数排第 1；同组增长最快的偏见缓解是 1.5 倍，因果发现是 2.4 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 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
• ICML 2026A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
• ICLR 2026Bures Generalized Category Discovery
• CVPR 2026Learning by Analogy A Causal Framework for Compositional Generalization
• NeurIPS 2025Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
• ICML 2025Bayesian Active Learning for Bivariate Causal Discovery
• ICLR 2025A Conditional Independence Test in the Presence of Discretization
• CVPR 2025Hyperbolic Category Discovery
• NeurIPS 2024Causal Bandits for Linear Structural Equation Models
• ICML 2024Adaptive Online Experimental Design for Causal Discovery
• ICLR 2024A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
• CVPR 2024Contrastive Mean-Shift Learning for Generalized Category Discovery
• NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
• ICML 2023Active causal structure learning with advice
• ICLR 2023Boosting Causal Discovery via Adaptive Sample Reweighting
• CVPR 2023Intrinsic Physical Concepts Discovery With Object-Centric Predictive Models
• NeurIPS 2026Act, Validate, Adapt: Closing the Causal Discovery-Control Loop
• ICML 2026Causal Discovery for Irregularly Time Series with Consistency Guarantees
• ICLR 2026Causal Discovery in the Wild: A Voting-Theoretic Ensemble Approach
• CVPR 2026PlanaReLoc Camera Relocalization in 3D Planar Primitives via Region-Based Structure

全部因果发现论文

共 150 篇，第 2 / 3 页

51. NeurIPS 2026Validating Causal Abstraction Metrics on Simulated Complex Systems
52. NeurIPS 2026Who caused $Y$? Local identifiability for learning causal parents
53. NeurIPS 2026xWhy: Causal Learning from Explanations
54. NeurIPS 2025Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
55. NeurIPS 2025Causal Discovery and Inference through Next-Token Prediction
56. NeurIPS 2025Causal Discovery over Clusters of Variables in Markovian Systems
57. NeurIPS 2025Causal Mixture Models: Characterization and Discovery
58. NeurIPS 2025CausalDynamics: A large‐scale benchmark for structural discovery of dynamical causal models
59. NeurIPS 2025Characterization and Learning of Causal Graphs from Hard Interventions
60. NeurIPS 2025Decoding Causal Structure: End-to-End Mediation Pathways Inference
61. NeurIPS 2025Differentiable Constraint-Based Causal Discovery
62. NeurIPS 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
63. NeurIPS 2025Differentiable Structure Learning and Causal Discovery for General Binary Data
64. NeurIPS 2025Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises
65. NeurIPS 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
66. NeurIPS 2025GLNCD: Graph-Level Novel Category Discovery
67. NeurIPS 2025Identifying Macro Causal Effects in C-DMGs over DMGs
68. NeurIPS 2025Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization
69. NeurIPS 2025It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
70. NeurIPS 2025Less Greedy Equivalence Search
71. NeurIPS 2025Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
72. NeurIPS 2025Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
73. NeurIPS 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
74. NeurIPS 2025Non-Stationary Structural Causal Bandits
75. NeurIPS 2025Novel Class Discovery for Point Cloud Segmentation via Joint Learning of Causal Representation and Reasoning
76. NeurIPS 2025Pattern-Guided Adaptive Prior for Structure Learning
77. NeurIPS 2025Practical Kernel Selection for Kernel-based Conditional Independence Test
78. NeurIPS 2025Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
79. NeurIPS 2025Revealing Multimodal Causality with Large Language Models
80. NeurIPS 2025Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
81. NeurIPS 2025SPARTAN: A Sparse Transformer World Model Attending to What Matters
82. NeurIPS 2025Structural Causal Bandits under Markov Equivalence
83. NeurIPS 2025The third pillar of causal analysis? A measurement perspective on causal representations
84. NeurIPS 2025Towards Identifiability of Hierarchical Temporal Causal Representation Learning
85. NeurIPS 2025UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems
86. NeurIPS 2025Universal Causal Inference in a Topos
87. NeurIPS 2025When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery
88. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
89. NeurIPS 2024Amortized Active Causal Induction with Deep Reinforcement Learning
90. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
91. NeurIPS 2024Causal Discovery from Event Sequences by Local Cause-Effect Attribution
92. NeurIPS 2024Causal discovery with endogenous context variables
93. NeurIPS 2024Causal vs. Anticausal merging of predictors
94. NeurIPS 2024Causal-learn: Causal Discovery in Python
95. NeurIPS 2024CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction
96. NeurIPS 2024ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions
97. NeurIPS 2024Clustering in Causal Attention Masking
98. NeurIPS 2024Differentiable Structure Learning with Partial Orders
99. NeurIPS 2024Discovery of the Hidden World with Large Language Models
100. NeurIPS 2024Do causal predictors generalize better to new domains?

常见问题

2023–2026 年四大会议一共有多少篇因果发现论文？

共 379 篇，按年份分别是 2023 年 59 篇、2024 年 82 篇、2025 年 99 篇、2026 年 139 篇。

NeurIPS 2026 有多少篇因果发现论文？

NeurIPS 2026 有 53 篇因果发现论文，上一届是 34 篇。

ICLR 2026 有多少篇因果发现论文？

ICLR 2026 有 25 篇因果发现论文，上一届是 27 篇。

ICML 2026 有多少篇因果发现论文？

ICML 2026 有 54 篇因果发现论文，上一届是 31 篇。

CVPR 2026 有多少篇因果发现论文？

CVPR 2026 有 7 篇因果发现论文，上一届是 7 篇。

哪个会议的因果发现论文最多？

NeurIPS，2023–2026 年共 150 篇。