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

108 papers, page 2 of 3

51. NeurIPS 2023Causal Fairness for Outcome Control
52. NeurIPS 2023Causal Imitability Under Context-Specific Independence Relations
53. NeurIPS 2023Causal normalizing flows: from theory to practice
54. NeurIPS 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
55. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets
56. NeurIPS 2023CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
57. NeurIPS 2023Collapsed Inference for Bayesian Deep Learning
58. NeurIPS 2023Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
59. NeurIPS 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
60. NeurIPS 2023Counterfactually Comparing Abstaining Classifiers
61. NeurIPS 2023Deep Non-line-of-sight Imaging from Under-scanning Measurements
62. NeurIPS 2023Detecting hidden confounding in observational data using multiple environments
63. NeurIPS 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
64. NeurIPS 2023Do Not Marginalize Mechanisms, Rather Consolidate!
65. NeurIPS 2023Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
66. NeurIPS 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
67. NeurIPS 2023Estimating Causal Effects Identifiable from a Combination of Observations and Experiments
68. NeurIPS 2023Fair Adaptive Experiments
69. NeurIPS 2023Flow Matching for Scalable Simulation-Based Inference
70. NeurIPS 2023GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference
71. NeurIPS 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
72. NeurIPS 2023High Precision Causal Model Evaluation with Conditional Randomization
73. NeurIPS 2023IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval
74. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
75. NeurIPS 2023Implicit Variational Inference for High-Dimensional Posteriors
76. NeurIPS 2023Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
77. NeurIPS 2023Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective
78. NeurIPS 2023Learning non-Markovian Decision-Making from State-only Sequences
79. NeurIPS 2023Learning Nonparametric Latent Causal Graphs with Unknown Interventions
80. NeurIPS 2023Learning Rate Free Sampling in Constrained Domains
81. NeurIPS 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecification
82. NeurIPS 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
83. NeurIPS 2023Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference
84. NeurIPS 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effects
85. NeurIPS 2023Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
86. NeurIPS 2023Nonparametric Identifiability of Causal Representations from Unknown Interventions
87. NeurIPS 2023On the Convergence of Black-Box Variational Inference
88. NeurIPS 2023Optimal Transport for Treatment Effect Estimation
89. NeurIPS 2023Pairwise Causality Guided Transformers for Event Sequences
90. NeurIPS 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
91. NeurIPS 2023Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties
92. NeurIPS 2023Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
93. NeurIPS 2023Probabilistic Inference in Reinforcement Learning Done Right
94. NeurIPS 2023PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
95. NeurIPS 2023Regularized Behavior Cloning for Blocking the Leakage of Past Action Information
96. NeurIPS 2023Rehearsal Learning for Avoiding Undesired Future
97. NeurIPS 2023Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
98. NeurIPS 2023Sharp Bounds for Generalized Causal Sensitivity Analysis
99. NeurIPS 2023Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
100. NeurIPS 2023Statistically Valid Variable Importance Assessment through Conditional Permutations

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.