因果推荐论文趋势 2023–2026

主要结论

• 因果推荐论文从 2023 年的 83 篇增长到 2026 年的 190 篇，三年间扩大到约 2.3 倍。
• NeurIPS 是发因果推荐论文最多的会议，2023–2026 年共 227 篇，占四个会议总量的 41%。
• 交叉方向里涨得最快的是多模态大模型：同时属于两个主题的论文从 2023 年的 1 篇增至 2026 年的 21 篇。
• 在「推荐与广告」的 5 个主题里，它的 2026 年论文数排第 3；同组增长最快的点击率预测是 2.6 倍，因果推荐是 2.3 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 Causal Decomposition Approach for Fair Contextual Multi-Armed Bandits
• ICLR 2026ActiveCQ: Active Estimation of Causal Quantities
• CVPR 2026Breaking Spurious Correlations Uncertainty-Driven Causal Transformers for AU Detection
• NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
• ICML 2025A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
• ICLR 2025Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
• CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
• NeurIPS 2024Causal Bandits for Linear Structural Equation Models
• ICML 2024A Sparsity Principle for Partially Observable Causal Representation Learning
• ICLR 2024Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
• CVPR 2024MuseChat: A Conversational Music Recommendation System for Videos
• NeurIPS 2023A Causal Framework for Decomposing Spurious Variations
• ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
• ICLR 2023Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
• CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
• NeurIPS 2026Bridging Image Restoration and Recognition via Causal Mediated Unrolling
• ICML 2026Causal Disentangled Anchor Learning for Scalable Fair Multi-view Clustering
• ICLR 2026Addressing divergent representations from causal interventions on neural networks
• CVPR 2026Bridge Basis-Driven Causal Inference Marries VFMs for Domain Generalization

全部因果推荐论文

共 548 篇，第 5 / 11 页

201. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
202. ICLR 2025Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark
203. ICLR 2025Doubly robust identification of treatment effects from multiple environments
204. ICLR 2025Efficient Causal Decision Making with One-sided Feedback
205. ICLR 2025Efficient Inference for Large Language Model-based Generative Recommendation
206. ICLR 2025Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
207. ICLR 2025From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy
208. ICLR 2025HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
209. ICLR 2025Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
210. ICLR 2025Interpretable Causal Representation Learning for Biological Data in the Pathway Space
211. ICLR 2025Language Representations Can be What Recommenders Need: Findings and Potentials
212. ICLR 2025Learning Causal Alignment for Reliable Disease Diagnosis
213. ICLR 2025Multimodal Quantitative Language for Generative Recommendation
214. ICLR 2025Neural Causal Graph for Interpretable and Intervenable Classification
215. ICLR 2025On the Identification of Temporal Causal Representation with Instantaneous Dependence
216. ICLR 2025RecFlow: An Industrial Full Flow Recommendation Dataset
217. ICLR 2025Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates
218. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
219. ICLR 2025Selective Induction Heads: How Transformers Select Causal Structures in Context
220. ICLR 2025SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
221. ICLR 2025Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
222. ICLR 2025Standardizing Structural Causal Models
223. ICLR 2025The Geometry of Categorical and Hierarchical Concepts in Large Language Models
224. ICLR 2025Unifying Causal Representation Learning with the Invariance Principle
225. ICML 2025A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
226. ICML 2025A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
227. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
228. ICML 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
229. ICML 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
230. ICML 2025Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?
231. ICML 2025Causal Abstraction Inference under Lossy Representations
232. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
233. ICML 2025Causal Attribution Analysis for Continuous Outcomes
234. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
235. ICML 2025Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning
236. ICML 2025Causal Logistic Bandits with Counterfactual Fairness Constraints
237. ICML 2025Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models
238. ICML 2025Distinguishing Cause from Effect with Causal Velocity Models
239. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
240. ICML 2025ENSUR: Equitable and Statistically Unbiased Recommendation
241. ICML 2025FairPFN: A Tabular Foundation Model for Causal Fairness
242. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
243. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
244. ICML 2025Generalized Random Forests Using Fixed-Point Trees
245. ICML 2025Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments
246. ICML 2025Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
247. ICML 2025Identifying biological perturbation targets through causal differential networks
248. ICML 2025Identifying Causal Direction via Variational Bayesian Compression
249. ICML 2025Integer Programming for Generalized Causal Bootstrap Designs
250. ICML 2025Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors

常见问题

2023–2026 年四大会议一共有多少篇因果推荐论文？

共 548 篇，按年份分别是 2023 年 83 篇、2024 年 123 篇、2025 年 152 篇、2026 年 190 篇。

NeurIPS 2026 有多少篇因果推荐论文？

NeurIPS 2026 有 75 篇因果推荐论文，上一届是 69 篇。

ICLR 2026 有多少篇因果推荐论文？

ICLR 2026 有 43 篇因果推荐论文，上一届是 32 篇。

ICML 2026 有多少篇因果推荐论文？

ICML 2026 有 56 篇因果推荐论文，上一届是 49 篇。

CVPR 2026 有多少篇因果推荐论文？

CVPR 2026 有 16 篇因果推荐论文，上一届是 2 篇。

哪个会议的因果推荐论文最多？

NeurIPS，2023–2026 年共 227 篇。