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 10 / 11 页

451. NeurIPS 2024Measuring Goal-Directedness
452. NeurIPS 2024Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
453. NeurIPS 2024Mitigating Object Hallucination via Concentric Causal Attention
454. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
455. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
456. NeurIPS 2024Proximal Causal Inference With Text Data
457. NeurIPS 2024PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
458. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
459. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
460. NeurIPS 2024Smoke and Mirrors in Causal Downstream Tasks
461. NeurIPS 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
462. NeurIPS 2024Towards Understanding Extrapolation: a Causal Lens
463. NeurIPS 2024Unified Covariate Adjustment for Causal Inference
464. NeurIPS 2024Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
465. NeurIPS 2024Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms
466. CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
467. CVPR 2023Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression
468. CVPR 2023Good Is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification
469. CVPR 2023Guided Recommendation for Model Fine-Tuning
470. CVPR 2023Layout-Based Causal Inference for Object Navigation
471. CVPR 2023Meta-Causal Learning for Single Domain Generalization
472. CVPR 2023Multi-View Adversarial Discriminator: Mine the Non-Causal Factors for Object Detection in Unseen Domains
473. ICLR 2023Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
474. ICLR 2023Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
475. ICLR 2023Causal Confusion and Reward Misidentification in Preference-Based Reward Learning
476. ICLR 2023Causal Estimation for Text Data with (Apparent) Overlap Violations
477. ICLR 2023Causal Imitation Learning via Inverse Reinforcement Learning
478. ICLR 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
479. ICLR 2023Combinatorial Pure Exploration of Causal Bandits
480. ICLR 2023Estimating individual treatment effects under unobserved confounding using binary instruments
481. ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
482. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
483. ICLR 2023Learning to Induce Causal Structure
484. ICLR 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
485. ICLR 2023Model-based Causal Bayesian Optimization
486. ICLR 2023Neural Causal Models for Counterfactual Identification and Estimation
487. ICLR 2023Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
488. ICLR 2023ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
489. ICLR 2023Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise
490. ICLR 2023StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
491. ICLR 2023Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
492. ICML 2023Active causal structure learning with advice
493. ICML 2023Additive Causal Bandits with Unknown Graph
494. ICML 2023Approximate Causal Effect Identification under Weak Confounding
495. ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
496. ICML 2023Causal Bounds in Quasi-Markovian Graphs
497. ICML 2023Causal Isotonic Calibration for Heterogeneous Treatment Effects
498. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
499. ICML 2023Collaborative Causal Inference with Fair Incentives
500. ICML 2023Constrained Causal Bayesian Optimization

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。