Causal Recommendation papers, 2023–2026

Key findings

• Causal Recommendation went from 83 papers in 2023 to 190 in 2026, roughly 2.3x in three years.
• NeurIPS publishes the most Causal Recommendation work: 227 papers from 2023 to 2026, 41% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 1 in 2023 to 21 in 2026.
• Among the 5 topics under Recommenders and Ads, it ranks #3 by 2026 paper count; the fastest grower in the group, Click-Through Prediction, is up 2.6x versus 2.3x here.

Representative papers

Spread across years and venues so every edition shows up

• 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

All Causal Recommendation papers

123 papers, page 2 of 3

51. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
52. ICML 2024Multiply-Robust Causal Change Attribution
53. ICML 2024On Positivity Condition for Causal Inference
54. ICML 2024On the Embedding Collapse when Scaling up Recommendation Models
55. ICML 2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
56. ICML 2024On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation
57. ICML 2024Position: The Causal Revolution Needs Scientific Pragmatism
58. ICML 2024Prediction-powered Generalization of Causal Inferences
59. ICML 2024Reducing Balancing Error for Causal Inference via Optimal Transport
60. ICML 2024StableMask: Refining Causal Masking in Decoder-only Transformer
61. ICML 2024Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
62. ICML 2024Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
63. ICML 2024Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
64. ICML 2024Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints
65. ICML 2024Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias
66. ICML 2024Wukong: Towards a Scaling Law for Large-Scale Recommendation
67. NeurIPS 2024A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
68. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
69. NeurIPS 2024Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
70. NeurIPS 2024Analysing the Generalisation and Reliability of Steering Vectors
71. NeurIPS 2024BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
72. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
73. NeurIPS 2024Causal Contrastive Learning for Counterfactual Regression Over Time
74. NeurIPS 2024Causal Dependence Plots
75. NeurIPS 2024Causal Effect Identification in a Sub-Population with Latent Variables
76. NeurIPS 2024Causal Imitation for Markov Decision Processes: a Partial Identification Approach
77. NeurIPS 2024Causal Temporal Representation Learning with Nonstationary Sparse Transition
78. NeurIPS 2024Causal vs. Anticausal merging of predictors
79. NeurIPS 2024Clustering in Causal Attention Masking
80. NeurIPS 2024Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
81. NeurIPS 2024Consistency of Neural Causal Partial Identification
82. NeurIPS 2024CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
83. NeurIPS 2024Detecting and Measuring Confounding Using Causal Mechanism Shifts
84. NeurIPS 2024Disentangled Representation Learning in Non-Markovian Causal Systems
85. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
86. NeurIPS 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Data
87. NeurIPS 2024End-to-end Learnable Clustering for Intent Learning in Recommendation
88. NeurIPS 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
89. NeurIPS 2024Fast Proxy Experiment Design for Causal Effect Identification
90. NeurIPS 2024Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination
91. NeurIPS 2024Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
92. NeurIPS 2024From Causal to Concept-Based Representation Learning
93. NeurIPS 2024Hierarchical and Density-based Causal Clustering
94. NeurIPS 2024Higher-Order Causal Message Passing for Experimentation with Complex Interference
95. NeurIPS 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
96. NeurIPS 2024Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
97. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
98. NeurIPS 2024Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
99. NeurIPS 2024In Pursuit of Causal Label Correlations for Multi-label Image Recognition
100. NeurIPS 2024IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark

FAQ

How many Causal Recommendation papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

548 in total: 83 in 2023, 123 in 2024, 152 in 2025, 190 in 2026.

How many Causal Recommendation papers are at NeurIPS 2026?

NeurIPS 2026 has 75 Causal Recommendation papers, versus 69 the year before.

How many Causal Recommendation papers are at ICLR 2026?

ICLR 2026 has 43 Causal Recommendation papers, versus 32 the year before.

How many Causal Recommendation papers are at ICML 2026?

ICML 2026 has 56 Causal Recommendation papers, versus 49 the year before.

How many Causal Recommendation papers are at CVPR 2026?

CVPR 2026 has 16 Causal Recommendation papers, versus 2 the year before.

Which conference publishes the most Causal Recommendation papers?

NeurIPS, with 227 papers from 2023 to 2026.