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

57 papers, page 1 of 2

1. NeurIPS 2024A Local Method for Satisfying Interventional Fairness with Partially Known Causal Graphs
2. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
3. NeurIPS 2024Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation
4. NeurIPS 2024Analysing the Generalisation and Reliability of Steering Vectors
5. NeurIPS 2024BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
6. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
7. NeurIPS 2024Causal Contrastive Learning for Counterfactual Regression Over Time
8. NeurIPS 2024Causal Dependence Plots
9. NeurIPS 2024Causal Effect Identification in a Sub-Population with Latent Variables
10. NeurIPS 2024Causal Imitation for Markov Decision Processes: a Partial Identification Approach
11. NeurIPS 2024Causal Temporal Representation Learning with Nonstationary Sparse Transition
12. NeurIPS 2024Causal vs. Anticausal merging of predictors
13. NeurIPS 2024Clustering in Causal Attention Masking
14. NeurIPS 2024Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
15. NeurIPS 2024Consistency of Neural Causal Partial Identification
16. NeurIPS 2024CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
17. NeurIPS 2024Detecting and Measuring Confounding Using Causal Mechanism Shifts
18. NeurIPS 2024Disentangled Representation Learning in Non-Markovian Causal Systems
19. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
20. NeurIPS 2024End-To-End Causal Effect Estimation from Unstructured Natural Language Data
21. NeurIPS 2024End-to-end Learnable Clustering for Intent Learning in Recommendation
22. NeurIPS 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
23. NeurIPS 2024Fast Proxy Experiment Design for Causal Effect Identification
24. NeurIPS 2024Fine Tuning Out-of-Vocabulary Item Recommendation with User Sequence Imagination
25. NeurIPS 2024Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
26. NeurIPS 2024From Causal to Concept-Based Representation Learning
27. NeurIPS 2024Hierarchical and Density-based Causal Clustering
28. NeurIPS 2024Higher-Order Causal Message Passing for Experimentation with Complex Interference
29. NeurIPS 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
30. NeurIPS 2024Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
31. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
32. NeurIPS 2024Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
33. NeurIPS 2024In Pursuit of Causal Label Correlations for Multi-label Image Recognition
34. NeurIPS 2024IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
35. NeurIPS 2024Intervention and Conditioning in Causal Bayesian Networks
36. NeurIPS 2024Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
37. NeurIPS 2024Learning 1D Causal Visual Representation with De-focus Attention Networks
38. NeurIPS 2024Learning Identifiable Factorized Causal Representations of Cellular Responses
39. NeurIPS 2024Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
40. NeurIPS 2024Linear Causal Bandits: Unknown Graph and Soft Interventions
41. NeurIPS 2024Linear Causal Representation Learning from Unknown Multi-node Interventions
42. NeurIPS 2024Marginal Causal Flows for Validation and Inference
43. NeurIPS 2024Measuring Goal-Directedness
44. NeurIPS 2024Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
45. NeurIPS 2024Mitigating Object Hallucination via Concentric Causal Attention
46. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
47. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
48. NeurIPS 2024Proximal Causal Inference With Text Data
49. NeurIPS 2024PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
50. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner

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.