深度学习 · causal inference论文趋势 2023–2026

主要结论

• 深度学习 · causal inference论文从 2023 年的 108 篇降到 2026 年的 66 篇，热度在回落。
• NeurIPS 是发深度学习 · causal inference论文最多的会议，2023–2026 年共 196 篇，占四个会议总量的 62%。
• 交叉方向里涨得最快的是因果推荐：同时属于两个主题的论文从 2023 年的 27 篇增至 2026 年的 55 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 10；同组增长最快的扩散模型是 4.1 倍，深度学习 · causal inference是 0.6 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部深度学习 · causal inference论文

共 196 篇，第 4 / 4 页

151. NeurIPS 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
152. NeurIPS 2023Do Not Marginalize Mechanisms, Rather Consolidate!
153. NeurIPS 2023Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
154. NeurIPS 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
155. NeurIPS 2023Estimating Causal Effects Identifiable from a Combination of Observations and Experiments
156. NeurIPS 2023Fair Adaptive Experiments
157. NeurIPS 2023Flow Matching for Scalable Simulation-Based Inference
158. NeurIPS 2023GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference
159. NeurIPS 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
160. NeurIPS 2023High Precision Causal Model Evaluation with Conditional Randomization
161. NeurIPS 2023IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval
162. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
163. NeurIPS 2023Implicit Variational Inference for High-Dimensional Posteriors
164. NeurIPS 2023Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
165. NeurIPS 2023Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective
166. NeurIPS 2023Learning non-Markovian Decision-Making from State-only Sequences
167. NeurIPS 2023Learning Nonparametric Latent Causal Graphs with Unknown Interventions
168. NeurIPS 2023Learning Rate Free Sampling in Constrained Domains
169. NeurIPS 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecification
170. NeurIPS 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
171. NeurIPS 2023Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference
172. NeurIPS 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effects
173. NeurIPS 2023Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
174. NeurIPS 2023Nonparametric Identifiability of Causal Representations from Unknown Interventions
175. NeurIPS 2023On the Convergence of Black-Box Variational Inference
176. NeurIPS 2023Optimal Transport for Treatment Effect Estimation
177. NeurIPS 2023Pairwise Causality Guided Transformers for Event Sequences
178. NeurIPS 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
179. NeurIPS 2023Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties
180. NeurIPS 2023Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
181. NeurIPS 2023Probabilistic Inference in Reinforcement Learning Done Right
182. NeurIPS 2023PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
183. NeurIPS 2023Regularized Behavior Cloning for Blocking the Leakage of Past Action Information
184. NeurIPS 2023Rehearsal Learning for Avoiding Undesired Future
185. NeurIPS 2023Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
186. NeurIPS 2023Sharp Bounds for Generalized Causal Sensitivity Analysis
187. NeurIPS 2023Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
188. NeurIPS 2023Statistically Valid Variable Importance Assessment through Conditional Permutations
189. NeurIPS 2023StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
190. NeurIPS 2023Structured Neural Networks for Density Estimation and Causal Inference
191. NeurIPS 2023Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
192. NeurIPS 2023Tree Variational Autoencoders
193. NeurIPS 2023Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
194. NeurIPS 2023Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
195. NeurIPS 2023Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
196. NeurIPS 2023Zero-shot causal learning

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · causal inference论文？

共 316 篇，按年份分别是 2023 年 108 篇、2024 年 108 篇、2025 年 34 篇、2026 年 66 篇。

NeurIPS 2026 有多少篇深度学习 · causal inference论文？

NeurIPS 2026 有 28 篇深度学习 · causal inference论文，上一届是 9 篇。

ICLR 2026 有多少篇深度学习 · causal inference论文？

ICLR 2026 有 8 篇深度学习 · causal inference论文，上一届是 6 篇。

ICML 2026 有多少篇深度学习 · causal inference论文？

ICML 2026 有 26 篇深度学习 · causal inference论文，上一届是 18 篇。

CVPR 2026 有多少篇深度学习 · causal inference论文？

CVPR 2026 有 4 篇深度学习 · causal inference论文，上一届是 1 篇。

哪个会议的深度学习 · causal inference论文最多？

NeurIPS，2023–2026 年共 196 篇。