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

227 papers, page 1 of 5

1. NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
2. NeurIPS 2026A Faster Algorithm for the Half-Trek Criterion in Structural Causal Models
3. NeurIPS 2026ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
4. NeurIPS 2026AdaM-Rec: Adaptive Modality Routing for Multimodal Recommendation
5. NeurIPS 2026AdaSRU: Adaptive Source-Free Recommendation Unlearning via Gradient-Constrained Optimization
6. NeurIPS 2026An Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
7. NeurIPS 2026Artificial Aphasias in Lesioned Language Models
8. NeurIPS 2026Automated Causal Effect Estimation through Self-Evolving AI
9. NeurIPS 2026Bayesian Causal Stress Testing: Posterior Fragility of Treatment-Effect Conclusions
10. NeurIPS 2026Bridging Image Restoration and Recognition via Causal Mediated Unrolling
11. NeurIPS 2026CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series
12. NeurIPS 2026Causal Abstractions, Categorically Unified
13. NeurIPS 2026Causal Bias Detection in Generative Artifical Intelligence
14. NeurIPS 2026Causal Effect Identification with a Single Agnostic Proxy
15. NeurIPS 2026Causal Gradient Steering: Exposing Shortcut Gradients by Destroying Causal Signal
16. NeurIPS 2026Causal Inference for Sequential Settings under Interference and Latent Confounding
17. NeurIPS 2026Causal learning with the invariance principle
18. NeurIPS 2026Causal Multi-Task Demand Learning
19. NeurIPS 2026Causal pieces: analysing and improving spiking neural networks piece by piece
20. NeurIPS 2026Causal Representation Learning for Generalisable Recommendation
21. NeurIPS 2026Causal Survival Forests with Negative Controls
22. NeurIPS 2026Causal-VLM: Dense Causal Captioning in Videos
23. NeurIPS 2026Characterizing Underrepresentation in Generalizing Causal Survival Estimates
24. NeurIPS 2026Coarsening Linear Non-Gaussian Causal Models with Cycles
25. NeurIPS 2026Confounding-Aware Client Selection in Federated Learning via Causal Mediation Analysis
26. NeurIPS 2026CorridorLight: Cooperation as Task Negotiation with Causal Gating for Traffic Signal Control
27. NeurIPS 2026Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
28. NeurIPS 2026Data-Driven Covariate Selection for Nonparametric and Cycle-Agnostic Causal Effect Estimation
29. NeurIPS 2026Decision Path Tracing for Causal Analysis in Transformers
30. NeurIPS 2026Decomposing Effects in Neural Causal Models
31. NeurIPS 2026Detect What You Need: Chain-of-Causal Reasoning for 3D Intent Grounding
32. NeurIPS 2026DoAtlas-1: A Causal Compilation Paradigm for Clinical AI
33. NeurIPS 2026Emergent representations of graphical structure in mechanistic neural models of causal judgment
34. NeurIPS 2026From Document Layout to Causal Topology: An End-to-End Architecture for Temporal Causal Reasoning
35. NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
36. NeurIPS 2026Full-Sequence Masked Diffusion for Generative Recommendation
37. NeurIPS 2026Generalized Bayes for Causal Inference
38. NeurIPS 2026HARP: Training-Free Dual-Profile Agentic Communication for LLM-Based Recommendation
39. NeurIPS 2026Identifying Structural Biases from Causal Mechanism Shifts
40. NeurIPS 2026Improving Causal Explanations
41. NeurIPS 2026IntentLens: Grounding Underspecified Multimodal Queries for Recommendation via Tool-Augmented Reasoning
42. NeurIPS 2026Is Your LLM-as-a-Recommender Agent Trustable? LLMs' Recommendation is Easily Hacked by Biases (Preferences)
43. NeurIPS 2026Learning Contextual Causal Dynamics for Robust Exploration in Reinforcement Learning
44. NeurIPS 2026Learning Deployable Causal Action Geometry under Temporal Non-Stationarity
45. NeurIPS 2026LLM Routing Through the Lens of Recommendation: A Roadmap for Efficient AI Orchestration
46. NeurIPS 2026MARS: Multi-resolution Adaptive Routing for Sequential Recommendation
47. NeurIPS 2026MOCHA: Discovering Multi-Order Dynamic Causal Structure in Temporal Point Processes
48. NeurIPS 2026Multi-Objective Causal Bandits: Minimal Intervention Space and Policy-Level Learning
49. NeurIPS 2026Natural Jammers: When Non-Robust Features Become Antagonistic
50. NeurIPS 2026Neural Causal Models under Markov Equivalence

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.