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 3 of 4

101. ICML 2025Telling Peer Direct Effects from Indirect Effects in Observational Network Data
102. ICML 2025Tightening Causal Bounds via Covariate-Aware Optimal Transport
103. ICML 2025Towards Robustness and Explainability of Automatic Algorithm Selection
104. ICML 2025Towards the Causal Complete Cause of Multi-Modal Representation Learning
105. ICML 2025Unbiased Recommender Learning from Implicit Feedback via Weakly Supervised Learning
106. ICML 2024A Sparsity Principle for Partially Observable Causal Representation Learning
107. ICML 2024Causal Action Influence Aware Counterfactual Data Augmentation
108. ICML 2024Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
109. ICML 2024Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
110. ICML 2024Causal Effect Identification in LiNGAM Models with Latent Confounders
111. ICML 2024Causal Inference from Competing Treatments
112. ICML 2024Causal Inference out of Control: Estimating Performativity without Treatment Randomization
113. ICML 2024Causal Representation Learning from Multiple Distributions: A General Setting
114. ICML 2024Causal Representation Learning Made Identifiable by Grouping of Observational Variables
115. ICML 2024Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference
116. ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
117. ICML 2024Discovering Bias in Latent Space: An Unsupervised Debiasing Approach
118. ICML 2024Discovering Mixtures of Structural Causal Models from Time Series Data
119. ICML 2024Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
120. ICML 2024Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
121. ICML 2024Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning
122. ICML 2024From Geometry to Causality- Ricci Curvature and the Reliability of Causal Inference on Networks
123. ICML 2024Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis
124. ICML 2024How Transformers Learn Causal Structure with Gradient Descent
125. ICML 2024Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
126. ICML 2024Learning Causal Relations from Subsampled Time Series with Two Time-Slices
127. ICML 2024Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation
128. ICML 2024Local Causal Structure Learning in the Presence of Latent Variables
129. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
130. ICML 2024Multiply-Robust Causal Change Attribution
131. ICML 2024On Positivity Condition for Causal Inference
132. ICML 2024On the Embedding Collapse when Scaling up Recommendation Models
133. ICML 2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
134. ICML 2024On the Unexpected Effectiveness of Reinforcement Learning for Sequential Recommendation
135. ICML 2024Position: The Causal Revolution Needs Scientific Pragmatism
136. ICML 2024Prediction-powered Generalization of Causal Inferences
137. ICML 2024Reducing Balancing Error for Causal Inference via Optimal Transport
138. ICML 2024StableMask: Refining Causal Masking in Decoder-only Transformer
139. ICML 2024Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
140. ICML 2024Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
141. ICML 2024Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention
142. ICML 2024Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints
143. ICML 2024Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias
144. ICML 2024Wukong: Towards a Scaling Law for Large-Scale Recommendation
145. ICML 2023Active causal structure learning with advice
146. ICML 2023Additive Causal Bandits with Unknown Graph
147. ICML 2023Approximate Causal Effect Identification under Weak Confounding
148. ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
149. ICML 2023Causal Bounds in Quasi-Markovian Graphs
150. ICML 2023Causal Isotonic Calibration for Heterogeneous Treatment Effects

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.