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

175 papers, page 2 of 4

51. ICML 2026TimeSAE: Causal Sparse Decoding for Faithful Explanations of Black-Box Time Series Models
52. ICML 2026Treatment Responder Classification with Abstention
53. ICML 2026U$^3$CF: Unbiased, Unconfounding, and Unified Causal Framework for Multi-Target Domain Adaptation
54. ICML 2026Unveiling Prior-Data Fitted Networks on Causal Effect Estimation: Pre-Training or Fine-Tuning?
55. ICML 2026Use What You Know: Causal Foundation Models with Partial Graphs
56. ICML 2026WeDLM: Reconciling Diffusion Language Models with Standard Causal Attention for Fast Inference
57. ICML 2025A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
58. ICML 2025A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
59. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
60. ICML 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
61. ICML 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
62. ICML 2025Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?
63. ICML 2025Causal Abstraction Inference under Lossy Representations
64. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
65. ICML 2025Causal Attribution Analysis for Continuous Outcomes
66. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
67. ICML 2025Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning
68. ICML 2025Causal Logistic Bandits with Counterfactual Fairness Constraints
69. ICML 2025Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models
70. ICML 2025Distinguishing Cause from Effect with Causal Velocity Models
71. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
72. ICML 2025ENSUR: Equitable and Statistically Unbiased Recommendation
73. ICML 2025FairPFN: A Tabular Foundation Model for Causal Fairness
74. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
75. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
76. ICML 2025Generalized Random Forests Using Fixed-Point Trees
77. ICML 2025Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments
78. ICML 2025Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
79. ICML 2025Identifying biological perturbation targets through causal differential networks
80. ICML 2025Identifying Causal Direction via Variational Bayesian Compression
81. ICML 2025Integer Programming for Generalized Causal Bootstrap Designs
82. ICML 2025Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors
83. ICML 2025Isolated Causal Effects of Natural Language
84. ICML 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
85. ICML 2025Learning with Selectively Labeled Data from Multiple Decision-makers
86. ICML 2025LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
87. ICML 2025LLMScan: Causal Scan for LLM Misbehavior Detection
88. ICML 2025Local Identifying Causal Relations in the Presence of Latent Variables
89. ICML 2025Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
90. ICML 2025Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits
91. ICML 2025Multi-Objective Causal Bayesian Optimization
92. ICML 2025Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect
93. ICML 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
94. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
95. ICML 2025Preference Learning for AI Alignment: a Causal Perspective
96. ICML 2025Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies
97. ICML 2025RATE: Causal Explainability of Reward Models with Imperfect Counterfactuals
98. ICML 2025Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
99. ICML 2025Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation
100. ICML 2025Teaching Transformers Causal Reasoning through Axiomatic Training

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.