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 3 of 3

101. NeurIPS 2024Intervention and Conditioning in Causal Bayesian Networks
102. NeurIPS 2024Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
103. NeurIPS 2024Learning 1D Causal Visual Representation with De-focus Attention Networks
104. NeurIPS 2024Learning Identifiable Factorized Causal Representations of Cellular Responses
105. NeurIPS 2024Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
106. NeurIPS 2024Linear Causal Bandits: Unknown Graph and Soft Interventions
107. NeurIPS 2024Linear Causal Representation Learning from Unknown Multi-node Interventions
108. NeurIPS 2024Marginal Causal Flows for Validation and Inference
109. NeurIPS 2024Measuring Goal-Directedness
110. NeurIPS 2024Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
111. NeurIPS 2024Mitigating Object Hallucination via Concentric Causal Attention
112. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
113. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
114. NeurIPS 2024Proximal Causal Inference With Text Data
115. NeurIPS 2024PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
116. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
117. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
118. NeurIPS 2024Smoke and Mirrors in Causal Downstream Tasks
119. NeurIPS 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
120. NeurIPS 2024Towards Understanding Extrapolation: a Causal Lens
121. NeurIPS 2024Unified Covariate Adjustment for Causal Inference
122. NeurIPS 2024Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
123. NeurIPS 2024Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms

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.