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

26 papers, page 1 of 1

1. ICLR 2026Addressing divergent representations from causal interventions on neural networks
2. ICLR 2026Causal-Steer: Disentangled Continuous Style Control without Parallel Corpora
3. ICLR 2026Coarse-to-Fine Learning of Dynamic Causal Structures
4. ICLR 2026Contextual Causal Bayesian Optimisation
5. ICLR 2026Counterfactual Structural Causal Bandits
6. ICLR 2026Exploratory Causal Inference in SAEnce
7. ICLR 2026On Measuring Influence in Avoiding Undesired Future
8. ICLR 2026Topological Causal Effects
9. ICLR 2025A Robust Method to Discover Causal or Anticausal Relation
10. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
11. ICLR 2025Doubly robust identification of treatment effects from multiple environments
12. ICLR 2025Incremental Causal Effect for Time to Treatment Initialization
13. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
14. ICLR 2025Standardizing Structural Causal Models
15. ICLR 2024Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
16. ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
17. ICLR 2024Causally Aligned Curriculum Learning
18. ICLR 2024Debiased Collaborative Filtering with Kernel-Based Causal Balancing
19. ICLR 2024Learning in reverse causal strategic environments with ramifications on two sided markets
20. ICLR 2024Treatment Effects Estimation By Uniform Transformer
21. ICLR 2023Causal Balancing for Domain Generalization
22. ICLR 2023Combinatorial Pure Exploration of Causal Bandits
23. ICLR 2023Estimating individual treatment effects under unobserved confounding using binary instruments
24. ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
25. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
26. ICLR 2023Learning to Induce Causal Structure

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.