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 5 of 11

201. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
202. ICLR 2025Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark
203. ICLR 2025Doubly robust identification of treatment effects from multiple environments
204. ICLR 2025Efficient Causal Decision Making with One-sided Feedback
205. ICLR 2025Efficient Inference for Large Language Model-based Generative Recommendation
206. ICLR 2025Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
207. ICLR 2025From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy
208. ICLR 2025HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
209. ICLR 2025Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
210. ICLR 2025Interpretable Causal Representation Learning for Biological Data in the Pathway Space
211. ICLR 2025Language Representations Can be What Recommenders Need: Findings and Potentials
212. ICLR 2025Learning Causal Alignment for Reliable Disease Diagnosis
213. ICLR 2025Multimodal Quantitative Language for Generative Recommendation
214. ICLR 2025Neural Causal Graph for Interpretable and Intervenable Classification
215. ICLR 2025On the Identification of Temporal Causal Representation with Instantaneous Dependence
216. ICLR 2025RecFlow: An Industrial Full Flow Recommendation Dataset
217. ICLR 2025Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates
218. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
219. ICLR 2025Selective Induction Heads: How Transformers Select Causal Structures in Context
220. ICLR 2025SLMRec: Distilling Large Language Models into Small for Sequential Recommendation
221. ICLR 2025Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
222. ICLR 2025Standardizing Structural Causal Models
223. ICLR 2025The Geometry of Categorical and Hierarchical Concepts in Large Language Models
224. ICLR 2025Unifying Causal Representation Learning with the Invariance Principle
225. ICML 2025A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
226. ICML 2025A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
227. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
228. ICML 2025ActionPiece: Contextually Tokenizing Action Sequences for Generative Recommendation
229. ICML 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach
230. ICML 2025Beyond Self-Interest: How Group Strategies Reshape Content Creation in Recommendation Platforms?
231. ICML 2025Causal Abstraction Inference under Lossy Representations
232. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
233. ICML 2025Causal Attribution Analysis for Continuous Outcomes
234. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
235. ICML 2025Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning
236. ICML 2025Causal Logistic Bandits with Counterfactual Fairness Constraints
237. ICML 2025Data-Driven Selection of Instrumental Variables for Additive Nonlinear, Constant Effects Models
238. ICML 2025Distinguishing Cause from Effect with Causal Velocity Models
239. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
240. ICML 2025ENSUR: Equitable and Statistically Unbiased Recommendation
241. ICML 2025FairPFN: A Tabular Foundation Model for Causal Fairness
242. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
243. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
244. ICML 2025Generalized Random Forests Using Fixed-Point Trees
245. ICML 2025Generalizing Causal Effects from Randomized Controlled Trials to Target Populations across Diverse Environments
246. ICML 2025Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees
247. ICML 2025Identifying biological perturbation targets through causal differential networks
248. ICML 2025Identifying Causal Direction via Variational Bayesian Compression
249. ICML 2025Integer Programming for Generalized Causal Bootstrap Designs
250. ICML 2025Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors

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.