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 1 of 3

1. CVPR 2023CafeBoost: Causal Feature Boost To Eliminate Task-Induced Bias for Class Incremental Learning
2. CVPR 2023Meta-Causal Learning for Single Domain Generalization
3. ICLR 2023Causal Balancing for Domain Generalization
4. ICLR 2023Combinatorial Pure Exploration of Causal Bandits
5. ICLR 2023Estimating individual treatment effects under unobserved confounding using binary instruments
6. ICLR 2023GEASS: Neural causal feature selection for high-dimensional biological data
7. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
8. ICLR 2023Learning to Induce Causal Structure
9. ICML 2023Active causal structure learning with advice
10. ICML 2023Active Learning based Structural Inference
11. ICML 2023Approximate Causal Effect Identification under Weak Confounding
12. ICML 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
13. ICML 2023Causal Bounds in Quasi-Markovian Graphs
14. ICML 2023Causal Isotonic Calibration for Heterogeneous Treatment Effects
15. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
16. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
17. ICML 2023Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
18. ICML 2023Collaborative Causal Inference with Fair Incentives
19. ICML 2023Conditions and Assumptions for Constraint-based Causal Structure Learning
20. ICML 2023Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
21. ICML 2023ContraBAR: Contrastive Bayes-Adaptive Deep RL
22. ICML 2023Covariate balancing using the integral probability metric for causal inference
23. ICML 2023Estimating Causal Effects using a Multi-task Deep Ensemble
24. ICML 2023Estimating Possible Causal Effects with Latent Variables via Adjustment
25. ICML 2023Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space
26. ICML 2023Linear Causal Disentanglement via Interventions
27. ICML 2023Neural Algorithmic Reasoning with Causal Regularisation
28. ICML 2023On Data Manifolds Entailed by Structural Causal Models
29. ICML 2023On the Relationship Between Explanation and Prediction: A Causal View
30. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
31. ICML 2023Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
32. ICML 2023Towards Trustworthy Explanation: On Causal Rationalization
33. ICML 2023Which Invariance Should We Transfer? A Causal Minimax Learning Approach
34. NeurIPS 2023A Causal Framework for Decomposing Spurious Variations
35. NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
36. NeurIPS 2023A Measure-Theoretic Axiomatisation of Causality
37. NeurIPS 2023A Unified Detection Framework for Inference-Stage Backdoor Defenses
38. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
39. NeurIPS 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effect
40. NeurIPS 2023Analysis of Variance of Multiple Causal Networks
41. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
42. NeurIPS 2023ARTree: A Deep Autoregressive Model for Phylogenetic Inference
43. NeurIPS 2023Assumption violations in causal discovery and the robustness of score matching
44. NeurIPS 2023Attention as Implicit Structural Inference
45. NeurIPS 2023Balancing Risk and Reward: A Batched-Bandit Strategy for Automated Phased Release
46. NeurIPS 2023Brain-like Flexible Visual Inference by Harnessing Feedback Feedforward Alignment
47. NeurIPS 2023C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
48. NeurIPS 2023Causal Component Analysis
49. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
50. NeurIPS 2023Causal discovery from observational and interventional data across multiple environments

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.