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 3 / 11 页

101. ICML 2026Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
102. ICML 2026Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
103. ICML 2026Spiked-CFR: Causal Representation Learning from LLMs via Wasserstein Projection Pursuit
104. ICML 2026Stem: Rethinking Causal Information Flow in Sparse Attention
105. ICML 2026Streaming Covariate Balancing via Discrepancy-Based Feature Coresets
106. ICML 2026SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation
107. ICML 2026Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
108. ICML 2026The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions
109. ICML 2026The Fairness Hierarchy: A viewpoint from causal inference
110. ICML 2026TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
111. ICML 2026Treatment Responder Classification with Abstention
112. ICML 2026U$^3$CF: Unbiased, Unconfounding, and Unified Causal Framework for Multi-Target Domain Adaptation
113. ICML 2026Unveiling Prior-Data Fitted Networks on Causal Effect Estimation: Pre-Training or Fine-Tuning?
114. ICML 2026Use What You Know: Causal Foundation Models with Partial Graphs
115. ICML 2026WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference
116. NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
117. NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
118. NeurIPS 2026ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
119. NeurIPS 2026AdaM-Rec: Adaptive Modality Routing for Multimodal Recommendation
120. NeurIPS 2026AdaSRU: Adaptive Source-Free Recommendation Unlearning via Gradient-Constrained Optimization
121. NeurIPS 2026An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
122. NeurIPS 2026Artificial Aphasias in Lesioned Language Models
123. NeurIPS 2026Automated Causal Effect Estimation through Self-Evolving AI
124. NeurIPS 2026Bayesian Causal Stress Testing: Posterior Fragility of Treatment-Effect Conclusions
125. NeurIPS 2026Bridging Image Restoration and Recognition via Causal Mediated Unrolling
126. NeurIPS 2026CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series
127. NeurIPS 2026Causal Abstractions, Categorically Unified
128. NeurIPS 2026Causal Bias Detection in Generative Artifical Intelligence
129. NeurIPS 2026Causal Effect Identification with a Single Agnostic Proxy
130. NeurIPS 2026Causal Gradient Steering: Exposing Shortcut Gradients by Destroying Causal Signal
131. NeurIPS 2026Causal Inference for Sequential Settings under Interference and Latent Confounding
132. NeurIPS 2026Causal learning with the invariance principle
133. NeurIPS 2026Causal Multi-Task Demand Learning
134. NeurIPS 2026Causal pieces: analysing and improving spiking neural networks piece by piece
135. NeurIPS 2026Causal Representation Learning for Generalisable Recommendation
136. NeurIPS 2026Causal Survival Forests with Negative Controls
137. NeurIPS 2026Causal-VLM: Dense Causal Captioning in Videos
138. NeurIPS 2026Characterizing Underrepresentation in Generalizing Causal Survival Estimates
139. NeurIPS 2026Coarsening Linear Non-Gaussian Causal Models with Cycles
140. NeurIPS 2026Confounding-Aware Client Selection in Federated Learning via Causal Mediation Analysis
141. NeurIPS 2026CorridorLight: Cooperation as Task Negotiation with Causal Gating for Traffic Signal Control
142. NeurIPS 2026Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
143. NeurIPS 2026Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation
144. NeurIPS 2026Decision Path Tracing for Causal Analysis in Transformers
145. NeurIPS 2026Decomposing Effects in Neural Causal Models
146. NeurIPS 2026Detect What You Need: Chain-of-Causal Reasoning for 3D Intent Grounding
147. NeurIPS 2026DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
148. NeurIPS 2026Emergent representations of graphical structure in mechanistic neural models of causal judgment
149. NeurIPS 2026From Document Layout to Causal Topology: An End-to-End Architecture for Temporal Causal Reasoning
150. NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers

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。