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

152 papers, page 1 of 4

1. CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
2. CVPR 2025Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
3. ICLR 2025A Causal Lens for Learning Long-term Fair Policies
4. ICLR 2025A Robust Method to Discover Causal or Anticausal Relation
5. ICLR 2025Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
6. ICLR 2025Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables
7. ICLR 2025Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference
8. ICLR 2025Causal Identification for Complex Functional Longitudinal Studies
9. ICLR 2025Causal Representation Learning from Multimodal Biomedical Observations
10. ICLR 2025ContextGNN: Beyond Two-Tower Recommendation Systems
11. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
12. ICLR 2025Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark
13. ICLR 2025Doubly robust identification of treatment effects from multiple environments
14. ICLR 2025Efficient Causal Decision Making with One-sided Feedback
15. ICLR 2025Efficient Inference for Large Language Model-based Generative Recommendation
16. ICLR 2025Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
17. ICLR 2025From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy
18. ICLR 2025HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
19. ICLR 2025Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
20. ICLR 2025Interpretable Causal Representation Learning for Biological Data in the Pathway Space
21. ICLR 2025Language Representations Can be What Recommenders Need: Findings and Potentials
22. ICLR 2025Learning Causal Alignment for Reliable Disease Diagnosis
23. ICLR 2025Multimodal Quantitative Language for Generative Recommendation
24. ICLR 2025Neural Causal Graph for Interpretable and Intervenable Classification
25. ICLR 2025On the Identification of Temporal Causal Representation with Instantaneous Dependence
26. ICLR 2025RecFlow: An Industrial Full Flow Recommendation Dataset
27. ICLR 2025Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates
28. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
29. ICLR 2025Selective Induction Heads: How Transformers Select Causal Structures in Context
30. ICLR 2025SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
31. ICLR 2025Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
32. ICLR 2025Standardizing Structural Causal Models
33. ICLR 2025The Geometry of Categorical and Hierarchical Concepts in Large Language Models
34. ICLR 2025Unifying Causal Representation Learning with the Invariance Principle
35. ICML 2025A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
36. ICML 2025A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
37. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
38. ICML 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
39. ICML 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
40. ICML 2025Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?
41. ICML 2025Causal Abstraction Inference under Lossy Representations
42. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
43. ICML 2025Causal Attribution Analysis for Continuous Outcomes
44. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
45. ICML 2025Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning
46. ICML 2025Causal Logistic Bandits with Counterfactual Fairness Constraints
47. ICML 2025Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models
48. ICML 2025Distinguishing Cause from Effect with Causal Velocity Models
49. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
50. ICML 2025ENSUR: Equitable and Statistically Unbiased Recommendation

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.