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

75 papers, page 2 of 2

51. NeurIPS 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effects
52. NeurIPS 2023Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
53. NeurIPS 2023Nonparametric Identifiability of Causal Representations from Unknown Interventions
54. NeurIPS 2023On the Convergence of Black-Box Variational Inference
55. NeurIPS 2023Optimal Transport for Treatment Effect Estimation
56. NeurIPS 2023Pairwise Causality Guided Transformers for Event Sequences
57. NeurIPS 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
58. NeurIPS 2023Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties
59. NeurIPS 2023Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
60. NeurIPS 2023Probabilistic Inference in Reinforcement Learning Done Right
61. NeurIPS 2023PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
62. NeurIPS 2023Regularized Behavior Cloning for Blocking the Leakage of Past Action Information
63. NeurIPS 2023Rehearsal Learning for Avoiding Undesired Future
64. NeurIPS 2023Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
65. NeurIPS 2023Sharp Bounds for Generalized Causal Sensitivity Analysis
66. NeurIPS 2023Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
67. NeurIPS 2023Statistically Valid Variable Importance Assessment through Conditional Permutations
68. NeurIPS 2023StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
69. NeurIPS 2023Structured Neural Networks for Density Estimation and Causal Inference
70. NeurIPS 2023Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
71. NeurIPS 2023Tree Variational Autoencoders
72. NeurIPS 2023Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
73. NeurIPS 2023Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
74. NeurIPS 2023Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
75. NeurIPS 2023Zero-shot causal learning

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.