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

21 papers, page 1 of 1

1. ICLR 2024A Neural Framework for Generalized Causal Sensitivity Analysis
2. ICLR 2024Adversarial Causal Bayesian Optimization
3. ICLR 2024Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
4. ICLR 2024Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
5. ICLR 2024Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder
6. ICLR 2024Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
7. ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
8. ICLR 2024Causally Aligned Curriculum Learning
9. ICLR 2024Conditional Instrumental Variable Regression with Representation Learning for Causal Inference
10. ICLR 2024Debiased Collaborative Filtering with Kernel-Based Causal Balancing
11. ICLR 2024Debiasing Algorithm through Model Adaptation
12. ICLR 2024Doubly Robust Proximal Causal Learning for Continuous Treatments
13. ICLR 2024Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
14. ICLR 2024Federated Recommendation with Additive Personalization
15. ICLR 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
16. ICLR 2024Identifiable Latent Polynomial Causal Models through the Lens of Change
17. ICLR 2024Learning in reverse causal strategic environments with ramifications on two sided markets
18. ICLR 2024Multi-View Causal Representation Learning with Partial Observability
19. ICLR 2024SpaCE: The Spatial Confounding Environment
20. ICLR 2024Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models
21. ICLR 2024Treatment Effects Estimation By Uniform Transformer

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.