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

25 papers, page 1 of 1

1. ICML 2023Active causal structure learning with advice
2. ICML 2023Active Learning based Structural Inference
3. ICML 2023Approximate Causal Effect Identification under Weak Confounding
4. ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
5. ICML 2023Causal Bounds in Quasi-Markovian Graphs
6. ICML 2023Causal Isotonic Calibration for Heterogeneous Treatment Effects
7. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
8. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
9. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
10. ICML 2023Collaborative Causal Inference with Fair Incentives
11. ICML 2023Conditions and Assumptions for Constraint-based Causal Structure Learning
12. ICML 2023Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
13. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
14. ICML 2023Covariate balancing using the integral probability metric for causal inference
15. ICML 2023Estimating Causal Effects using a Multi-task Deep Ensemble
16. ICML 2023Estimating Possible Causal Effects with Latent Variables via Adjustment
17. ICML 2023Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space
18. ICML 2023Linear Causal Disentanglement via Interventions
19. ICML 2023Neural Algorithmic Reasoning with Causal Regularisation
20. ICML 2023On Data Manifolds Entailed by Structural Causal Models
21. ICML 2023On the Relationship Between Explanation and Prediction: A Causal View
22. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
23. ICML 2023Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
24. ICML 2023Towards Trustworthy Explanation: On Causal Rationalization
25. ICML 2023Which Invariance Should We Transfer? A Causal Minimax Learning Approach

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.