Deep learning · causal inference papers, 2023–2026

Key findings

• Deep learning · causal inference fell from 108 papers in 2023 to 66 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · causal inference work: 196 papers from 2023 to 2026, 62% of the four-venue total.
• The fastest-growing overlap is with Causal Recommendation: papers tagged with both rose from 27 in 2023 to 55 in 2026.
• Among the 11 topics under Deep Learning, it ranks #10 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.6x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026From Groups to Rings: Causal Evidence for Algebraic Decomposition in Grokked Transformers
• ICML 2026Towards a Holistic Understanding of Selection Bias for Causal Effect Identification
• ICLR 2026Causal-Steer: Disentangled Continuous Style Control without Parallel Corpora
• CVPR 2026Breaking Spurious Correlations Uncertainty-Driven Causal Transformers for AU Detection
• NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
• ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
• ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
• CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
• NeurIPS 2024A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding
• ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
• ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
• CVPR 2024Bi-Causal: Group Activity Recognition via Bidirectional Causality
• NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
• ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
• ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
• CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
• NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
• ICML 2026Caracal: Causal Architecture via Spectral Mixing
• ICLR 2026Counterfactual Structural Causal Bandits
• CVPR 2026Bridge Basis-Driven Causal Inference Marries VFMs for Domain Generalization

All Deep learning · causal inference papers

316 papers, page 4 of 7

151. NeurIPS 2024Disentangled Representation Learning in Non-Markovian Causal Systems
152. NeurIPS 2024Do causal predictors generalize better to new domains?
153. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
154. NeurIPS 2024Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments
155. NeurIPS 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
156. NeurIPS 2024Efficient Policy Evaluation Across Multiple Different Experimental Datasets
157. NeurIPS 2024Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing
158. NeurIPS 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
159. NeurIPS 2024Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation
160. NeurIPS 2024Fast Proxy Experiment Design for Causal Effect Identification
161. NeurIPS 2024Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
162. NeurIPS 2024From Causal to Concept-Based Representation Learning
163. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
164. NeurIPS 2024Hierarchical and Density-based Causal Clustering
165. NeurIPS 2024Higher-Order Causal Message Passing for Experimentation with Complex Interference
166. NeurIPS 2024Identifiability Analysis of Linear ODE Systems with Hidden Confounders
167. NeurIPS 2024Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
168. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
169. NeurIPS 2024Identifying General Mechanism Shifts in Linear Causal Representations
170. NeurIPS 2024Intervention and Conditioning in Causal Bayesian Networks
171. NeurIPS 2024Interventional Causal Discovery in a Mixture of DAGs
172. NeurIPS 2024Learning Discrete Concepts in Latent Hierarchical Models
173. NeurIPS 2024Learning Discrete Latent Variable Structures with Tensor Rank Conditions
174. NeurIPS 2024Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
175. NeurIPS 2024Learning Mixtures of Unknown Causal Interventions
176. NeurIPS 2024Learning the Optimal Policy for Balancing Short-Term and Long-Term Rewards
177. NeurIPS 2024Linear Causal Representation Learning from Unknown Multi-node Interventions
178. NeurIPS 2024Marginal Causal Flows for Validation and Inference
179. NeurIPS 2024Markov Equivalence and Consistency in Differentiable Structure Learning
180. NeurIPS 2024Marrying Causal Representation Learning with Dynamical Systems for Science
181. NeurIPS 2024Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making
182. NeurIPS 2024Natural Counterfactuals With Necessary Backtracking
183. NeurIPS 2024Off-policy estimation with adaptively collected data: the power of online learning
184. NeurIPS 2024On Causal Discovery in the Presence of Deterministic Relations
185. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
186. NeurIPS 2024On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function
187. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
188. NeurIPS 2024Optimization-based Causal Estimation from Heterogeneous Environments
189. NeurIPS 2024Ordering-Based Causal Discovery for Linear and Nonlinear Relations
190. NeurIPS 2024Partial Transportability for Domain Generalization
191. NeurIPS 2024Proximal Causal Inference With Text Data
192. NeurIPS 2024Qualitative Mechanism Independence
193. NeurIPS 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner
194. NeurIPS 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
195. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
196. NeurIPS 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventions
197. NeurIPS 2024Smoke and Mirrors in Causal Downstream Tasks
198. NeurIPS 2024Stochastic Optimization Algorithms for Instrumental Variable Regression with Streaming Data
199. NeurIPS 2024Structured Learning of Compositional Sequential Interventions
200. NeurIPS 2024Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems

FAQ

How many Deep learning · causal inference papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

316 in total: 108 in 2023, 108 in 2024, 34 in 2025, 66 in 2026.

How many Deep learning · causal inference papers are at NeurIPS 2026?

NeurIPS 2026 has 28 Deep learning · causal inference papers, versus 9 the year before.

How many Deep learning · causal inference papers are at ICLR 2026?

ICLR 2026 has 8 Deep learning · causal inference papers, versus 6 the year before.

How many Deep learning · causal inference papers are at ICML 2026?

ICML 2026 has 26 Deep learning · causal inference papers, versus 18 the year before.

How many Deep learning · causal inference papers are at CVPR 2026?

CVPR 2026 has 4 Deep learning · causal inference papers, versus 1 the year before.

Which conference publishes the most Deep learning · causal inference papers?

NeurIPS, with 196 papers from 2023 to 2026.