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 3 / 3 页

101. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
102. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
103. NeurIPS 2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
104. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
105. NeurIPS 2024Interventional Causal Discovery in a Mixture of DAGs
106. NeurIPS 2024Learning Discrete Concepts in Latent Hierarchical Models
107. NeurIPS 2024Learning Discrete Latent Variable Structures with Tensor Rank Conditions
108. NeurIPS 2024Learning Identifiable Factorized Causal Representations of Cellular Responses
109. NeurIPS 2024Learning Mixtures of Unknown Causal Interventions
110. NeurIPS 2024Learning the Latent Causal Structure for Modeling Label Noise
111. NeurIPS 2024MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
112. NeurIPS 2024On Causal Discovery in the Presence of Deterministic Relations
113. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
114. NeurIPS 2024On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function
115. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
116. NeurIPS 2024Optimal ablation for interpretability
117. NeurIPS 2024Ordering-Based Causal Discovery for Linear and Nonlinear Relations
118. NeurIPS 2024Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
119. NeurIPS 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
120. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
121. NeurIPS 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventions
122. NeurIPS 2024Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
123. NeurIPS 2024WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
124. NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
125. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
126. NeurIPS 2023Assumption violations in causal discovery and the robustness of score matching
127. NeurIPS 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
128. NeurIPS 2023Bayesian Active Causal Discovery with Multi-Fidelity Experiments
129. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
130. NeurIPS 2023Causal discovery from observational and interventional data across multiple environments
131. NeurIPS 2023Causal Discovery from Subsampled Time Series with Proxy Variables
132. NeurIPS 2023Causal Discovery in Semi-Stationary Time Series
133. NeurIPS 2023Causal Effect Identification in Uncertain Causal Networks
134. NeurIPS 2023Causal Imitability Under Context-Specific Independence Relations
135. NeurIPS 2023Causal Interpretation of Self-Attention in Pre-Trained Transformers
136. NeurIPS 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
137. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets
138. NeurIPS 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
139. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
140. NeurIPS 2023Identification of Nonlinear Latent Hierarchical Models
141. NeurIPS 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
142. NeurIPS 2023Learning Causal Models under Independent Changes
143. NeurIPS 2023Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
144. NeurIPS 2023Meek Separators and Their Applications in Targeted Causal Discovery
145. NeurIPS 2023Rotating Features for Object Discovery
146. NeurIPS 2023Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling
147. NeurIPS 2023Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
148. NeurIPS 2023Towards Distribution-Agnostic Generalized Category Discovery
149. NeurIPS 2023Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
150. NeurIPS 2023Unpaired Multi-Domain Causal Representation Learning

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。