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

83 papers, page 1 of 2

1. CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
2. CVPR 2023Demystifying Causal Features on Adversarial Examples and Causal Inoculation for Robust Network by Adversarial Instrumental Variable Regression
3. CVPR 2023Good Is Bad: Causality Inspired Cloth-Debiasing for Cloth-Changing Person Re-Identification
4. CVPR 2023Guided Recommendation for Model Fine-Tuning
5. CVPR 2023Layout-Based Causal Inference for Object Navigation
6. CVPR 2023Meta-Causal Learning for Single Domain Generalization
7. CVPR 2023Multi-View Adversarial Discriminator: Mine the Non-Causal Factors for Object Detection in Unseen Domains
8. ICLR 2023Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
9. ICLR 2023Can We Faithfully Represent Absence States to Compute Shapley Values on a DNN?
10. ICLR 2023Causal Confusion and Reward Misidentification in Preference-Based Reward Learning
11. ICLR 2023Causal Estimation for Text Data with (Apparent) Overlap Violations
12. ICLR 2023Causal Imitation Learning via Inverse Reinforcement Learning
13. ICLR 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
14. ICLR 2023Combinatorial Pure Exploration of Causal Bandits
15. ICLR 2023Estimating individual treatment effects under unobserved confounding using binary instruments
16. ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
17. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
18. ICLR 2023Learning to Induce Causal Structure
19. ICLR 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
20. ICLR 2023Model-based Causal Bayesian Optimization
21. ICLR 2023Neural Causal Models for Counterfactual Identification and Estimation
22. ICLR 2023Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
23. ICLR 2023ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
24. ICLR 2023Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise
25. ICLR 2023StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
26. ICLR 2023Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors
27. ICML 2023Active causal structure learning with advice
28. ICML 2023Additive Causal Bandits with Unknown Graph
29. ICML 2023Approximate Causal Effect Identification under Weak Confounding
30. ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
31. ICML 2023Causal Bounds in Quasi-Markovian Graphs
32. ICML 2023Causal Isotonic Calibration for Heterogeneous Treatment Effects
33. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
34. ICML 2023Collaborative Causal Inference with Fair Incentives
35. ICML 2023Constrained Causal Bayesian Optimization
36. ICML 2023Covariate balancing using the integral probability metric for causal inference
37. ICML 2023Curriculum Co-disentangled Representation Learning across Multiple Environments for Social Recommendation
38. ICML 2023Differentiable Multi-Target Causal Bayesian Experimental Design
39. ICML 2023DRCFS: Doubly Robust Causal Feature Selection
40. ICML 2023Estimating Possible Causal Effects with Latent Variables via Adjustment
41. ICML 2023Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting
42. ICML 2023How Bad is Top-$K$ Recommendation under Competing Content Creators?
43. ICML 2023Instrumental Variable Estimation of Average Partial Causal Effects
44. ICML 2023Interventional Causal Representation Learning
45. ICML 2023Linear Causal Disentanglement via Interventions
46. ICML 2023Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
47. ICML 2023Neural Algorithmic Reasoning with Causal Regularisation
48. ICML 2023New metrics and search algorithms for weighted causal DAGs
49. ICML 2023On Data Manifolds Entailed by Structural Causal Models
50. ICML 2023On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs

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.