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

548 papers, page 7 of 11

301. NeurIPS 2025Distributed mediation analysis with communication efficiency
302. NeurIPS 2025Do-PFN: In-Context Learning for Causal Effect Estimation
303. NeurIPS 2025Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem
304. NeurIPS 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
305. NeurIPS 2025Estimation of Treatment Effects in Extreme and Unobserved Data
306. NeurIPS 2025Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
307. NeurIPS 2025Fast exact recovery of noisy matrix from few entries: the infinity norm approach
308. NeurIPS 2025Faster Generic Identification in Tree-Shaped Structural Causal Models
309. NeurIPS 2025From Black-box to Causal-box: Towards Building More Interpretable Models
310. NeurIPS 2025Graph-Theoretic Insights into Bayesian Personalized Ranking for Recommendation
311. NeurIPS 2025HPSERec: A Hierarchical Partitioning and Stepwise Enhancement Framework for Long-tailed Sequential Recommendation
312. NeurIPS 2025Identifying Macro Causal Effects in C-DMGs over DMGs
313. NeurIPS 2025It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
314. NeurIPS 2025Make Information Diffusion Explainable: LLM-based Causal Framework for Diffusion Prediction
315. NeurIPS 2025Measure-Theoretic Anti-Causal Representation Learning
316. NeurIPS 2025Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
317. NeurIPS 2025Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
318. NeurIPS 2025Neuro-Spectral Architectures for Causal Physics-Informed Networks
319. NeurIPS 2025Non-Markovian Discrete Diffusion with Causal Language Models
320. NeurIPS 2025Non-Stationary Structural Causal Bandits
321. NeurIPS 2025ORBIT - Open Recommendation Benchmark for Reproducible Research with Hidden Tests
322. NeurIPS 2025P-Law: Predicting Quantitative Scaling Law with Entropy Guidance in Large Recommendation Models
323. NeurIPS 2025Path-Enhanced Contrastive Learning for Recommendation
324. NeurIPS 2025Photography Perspective Composition: Towards Aesthetic Perspective Recommendation
325. NeurIPS 2025Pinpointing Attention-Causal Communication in Language Models
326. NeurIPS 2025Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference
327. NeurIPS 2025Prediction-Powered Causal Inferences
328. NeurIPS 2025PUATE: Efficient ATE Estimation from Treated (Positive) and Unlabeled Units
329. NeurIPS 2025Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
330. NeurIPS 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
331. NeurIPS 2025Reward-oriented Causal Representation Learning
332. NeurIPS 2025SPARTAN: A Sparse Transformer World Model Attending to What Matters
333. NeurIPS 2025SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
334. NeurIPS 2025Think before Recommendation: Autonomous Reasoning-enhanced Recommender
335. NeurIPS 2025Towards Identifiability of Hierarchical Temporal Causal Representation Learning
336. NeurIPS 2025Towards Single-Source Domain Generalized Object Detection via Causal Visual Prompts
337. NeurIPS 2025Transferring Causal Effects using Proxies
338. NeurIPS 2025Tree of Preferences for Diversified Recommendation
339. NeurIPS 2025Turning Sand to Gold: Recycling Data to Bridge On-Policy and Off-Policy Learning via Causal Bound
340. NeurIPS 2025TV-Rec: Time-Variant Convolutional Filter for Sequential Recommendation
341. NeurIPS 2025Universal Causal Inference in a Topos
342. NeurIPS 2025When Causal Dynamics Matter: Adapting Causal Strategies through Meta-Aware Interventions
343. CVPR 2024Bi-Causal: Group Activity Recognition via Bidirectional Causality
344. CVPR 2024CaDeT: a Causal Disentanglement Approach for Robust Trajectory Prediction in Autonomous Driving
345. CVPR 2024Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection
346. CVPR 2024CausalPC: Improving the Robustness of Point Cloud Classification by Causal Effect Identification
347. CVPR 2024MuseChat: A Conversational Music Recommendation System for Videos
348. CVPR 2024ProMark: Proactive Diffusion Watermarking for Causal Attribution
349. ICLR 2024A Neural Framework for Generalized Causal Sensitivity Analysis
350. ICLR 2024Adversarial Causal Bayesian Optimization

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.