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

34 papers, page 1 of 1

1. CVPR 2025Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference
2. ICLR 2025A Robust Method to Discover Causal or Anticausal Relation
3. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
4. ICLR 2025Doubly robust identification of treatment effects from multiple environments
5. ICLR 2025Incremental Causal Effect for Time to Treatment Initialization
6. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
7. ICLR 2025Standardizing Structural Causal Models
8. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
9. ICML 2025Causal Abstraction Inference under Lossy Representations
10. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
11. ICML 2025Causal Attribution Analysis for Continuous Outcomes
12. ICML 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
13. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
14. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
15. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
16. ICML 2025Generalized Random Forests Using Fixed-Point Trees
17. ICML 2025Integer Programming for Generalized Causal Bootstrap Designs
18. ICML 2025Isolated Causal Effects of Natural Language
19. ICML 2025Learning with Selectively Labeled Data from Multiple Decision-makers
20. ICML 2025LLMScan: Causal Scan for LLM Misbehavior Detection
21. ICML 2025Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
22. ICML 2025Position: Probabilistic Modelling is Sufficient for Causal Inference
23. ICML 2025Reducing Confounding Bias without Data Splitting for Causal Inference via Optimal Transport
24. ICML 2025Telling Peer Direct Effects from Indirect Effects in Observational Network Data
25. ICML 2025Tightening Causal Bounds via Covariate-Aware Optimal Transport
26. NeurIPS 2025A Cautionary Tale on Integrating Studies with Disparate Outcome Measures for Causal Inference
27. NeurIPS 2025CCL: Causal-aware In-context Learning for Out-of-Distribution Generalization
28. NeurIPS 2025Data Fusion for Partial Identification of Causal Effects
29. NeurIPS 2025Decoding Causal Structure: End-to-End Mediation Pathways Inference
30. NeurIPS 2025Density Ratio-Free Doubly Robust Proxy Causal Learning
31. NeurIPS 2025Do-PFN: In-Context Learning for Causal Effect Estimation
32. NeurIPS 2025Prediction-Powered Causal Inferences
33. NeurIPS 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
34. NeurIPS 2025Structural Causal Bandits under Markov Equivalence

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.