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 11 / 11 页

501. ICML 2023Covariate balancing using the integral probability metric for causal inference
502. ICML 2023Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation
503. ICML 2023Differentiable Multi-Target Causal Bayesian Experimental Design
504. ICML 2023DRCFS: Doubly Robust Causal Feature Selection
505. ICML 2023Estimating Possible Causal Effects with Latent Variables via Adjustment
506. ICML 2023Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
507. ICML 2023How Bad is Top-$K$ Recommendation under Competing Content Creators?
508. ICML 2023Instrumental Variable Estimation of Average Partial Causal Effects
509. ICML 2023Interventional Causal Representation Learning
510. ICML 2023Linear Causal Disentanglement via Interventions
511. ICML 2023Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
512. ICML 2023Neural Algorithmic Reasoning with Causal Regularisation
513. ICML 2023New metrics and search algorithms for weighted causal DAGs
514. ICML 2023On Data Manifolds Entailed by Structural Causal Models
515. ICML 2023On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
516. ICML 2023On the Identifiability and Estimation of Causal Location-Scale Noise Models
517. ICML 2023Performative Recommendation: Diversifying Content via Strategic Incentives
518. ICML 2023Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
519. ICML 2023Propensity Matters: Measuring and Enhancing Balancing for Recommendation
520. ICML 2023Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
521. ICML 2023Towards Trustworthy Explanation: On Causal Rationalization
522. ICML 2023Which Invariance Should We Transfer? A Causal Minimax Learning Approach
523. NeurIPS 2023A Causal Framework for Decomposing Spurious Variations
524. NeurIPS 2023A Measure-Theoretic Axiomatisation of Causality
525. NeurIPS 2023A Multi-modal Global Instance Tracking Benchmark (MGIT): Better Locating Target in Complex Spatio-temporal and Causal Relationship
526. NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
527. NeurIPS 2023Cascading Bandits: Optimizing Recommendation Frequency in Delayed Feedback Environments
528. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
529. NeurIPS 2023Causal Effect Identification in Uncertain Causal Networks
530. NeurIPS 2023D-Separation for Causal Self-Explanation
531. NeurIPS 2023Do Not Marginalize Mechanisms, Rather Consolidate!
532. NeurIPS 2023Estimating Propensity for Causality-based Recommendation without Exposure Data
533. NeurIPS 2023Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
534. NeurIPS 2023Generator Identification for Linear SDEs with Additive and Multiplicative Noise
535. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
536. NeurIPS 2023Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
537. NeurIPS 2023Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization
538. NeurIPS 2023K-Nearest-Neighbor Local Sampling Based Conditional Independence Testing
539. NeurIPS 2023Learning Causal Models under Independent Changes
540. NeurIPS 2023Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
541. NeurIPS 2023Moral Responsibility for AI Systems
542. NeurIPS 2023On Learning Necessary and Sufficient Causal Graphs
543. NeurIPS 2023On the Relationship Between Relevance and Conflict in Online Social Link Recommendations
544. NeurIPS 2023Optimal Treatment Regimes for Proximal Causal Learning
545. NeurIPS 2023REASONER: An Explainable Recommendation Dataset with Comprehensive Labeling Ground Truths
546. NeurIPS 2023Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions
547. NeurIPS 2023Theoretically Guaranteed Bidirectional Data Rectification for Robust Sequential Recommendation
548. 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。