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 6 / 11 页

251. ICML 2025Isolated Causal Effects of Natural Language
252. ICML 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
253. ICML 2025Learning with Selectively Labeled Data from Multiple Decision-makers
254. ICML 2025LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
255. ICML 2025LLMScan: Causal Scan for LLM Misbehavior Detection
256. ICML 2025Local Identifying Causal Relations in the Presence of Latent Variables
257. ICML 2025Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
258. ICML 2025Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits
259. ICML 2025Multi-Objective Causal Bayesian Optimization
260. ICML 2025Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect
261. ICML 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
262. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
263. ICML 2025Preference Learning for AI Alignment: a Causal Perspective
264. ICML 2025Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies
265. ICML 2025RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals
266. ICML 2025Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
267. ICML 2025Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation
268. ICML 2025Teaching Transformers Causal Reasoning through Axiomatic Training
269. ICML 2025Telling Peer Direct Effects from Indirect Effects in Observational Network Data
270. ICML 2025Tightening Causal Bounds via Covariate-Aware Optimal Transport
271. ICML 2025Towards Robustness and Explainability of Automatic Algorithm Selection
272. ICML 2025Towards the Causal Complete Cause of Multi-Modal Representation Learning
273. ICML 2025Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning
274. NeurIPS 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
275. NeurIPS 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
276. NeurIPS 2025A Unified Framework for the Transportability of Population-Level Causal Measures
277. NeurIPS 2025An Analysis of Causal Effect Estimation using Outcome Invariant Data Augmentation
278. NeurIPS 2025BAM-ICL: Causal Hijacking In-Context Learning with Budgeted Adversarial Manipulation
279. NeurIPS 2025Beyond the Average: Distributional Causal Inference under Imperfect Compliance
280. NeurIPS 2025Causal Climate Emulation with Bayesian Filtering
281. NeurIPS 2025Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
282. NeurIPS 2025Causal Explanation-Guided Learning for Organ Allocation
283. NeurIPS 2025Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers
284. NeurIPS 2025Causal LLM Routing: End-to-End Regret Minimization from Observational Data
285. NeurIPS 2025Causal Spatio-Temporal Prediction: An Effective and Efficient Multi-Modal Approach
286. NeurIPS 2025Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
287. NeurIPS 2025Causality Meets the Table: Debiasing LLMs for Faithful TableQA via Front-Door Intervention
288. NeurIPS 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
289. NeurIPS 2025CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations
290. NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
291. NeurIPS 2025CMoB: Modality Valuation via Causal Effect for Balanced Multimodal Learning
292. NeurIPS 2025Conformal Prediction for Causal Effects of Continuous Treatments
293. NeurIPS 2025Confounding Robust Deep Reinforcement Learning: A Causal Approach
294. NeurIPS 2025Curious Causality-Seeking Agents Learn Meta Causal World
295. NeurIPS 2025CVGL: Causal Learning and Geometric Topology
296. NeurIPS 2025Data Fusion for Partial Identification of Causal Effects
297. NeurIPS 2025DeCaFlow: A deconfounding causal generative model
298. NeurIPS 2025Decoding Causal Structure: End-to-End Mediation Pathways Inference
299. NeurIPS 2025Density Ratio-Free Doubly Robust Proxy Causal Learning
300. NeurIPS 2025Disentangling misreporting from genuine adaptation in strategic settings: a causal approach

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。