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 1 of 2

1. NeurIPS 2023A Causal Framework for Decomposing Spurious Variations
2. NeurIPS 2023A Cross-Moment Approach for Causal Effect Estimation
3. NeurIPS 2023A Measure-Theoretic Axiomatisation of Causality
4. NeurIPS 2023A Unified Detection Framework for Inference-Stage Backdoor Defenses
5. NeurIPS 2023A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
6. NeurIPS 2023An Efficient Doubly-Robust Test for the Kernel Treatment Effect
7. NeurIPS 2023Analysis of Variance of Multiple Causal Networks
8. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
9. NeurIPS 2023ARTree: A Deep Autoregressive Model for Phylogenetic Inference
10. NeurIPS 2023Assumption violations in causal discovery and the robustness of score matching
11. NeurIPS 2023Attention as Implicit Structural Inference
12. NeurIPS 2023Balancing Risk and Reward: A Batched-Bandit Strategy for Automated Phased Release
13. NeurIPS 2023Brain-like Flexible Visual Inference by Harnessing Feedback Feedforward Alignment
14. NeurIPS 2023C-Disentanglement: Discovering Causally-Independent Generative Factors under an Inductive Bias of Confounder
15. NeurIPS 2023Causal Component Analysis
16. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
17. NeurIPS 2023Causal discovery from observational and interventional data across multiple environments
18. NeurIPS 2023Causal Fairness for Outcome Control
19. NeurIPS 2023Causal Imitability Under Context-Specific Independence Relations
20. NeurIPS 2023Causal normalizing flows: from theory to practice
21. NeurIPS 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
22. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets
23. NeurIPS 2023CLIP-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
24. NeurIPS 2023Collapsed Inference for Bayesian Deep Learning
25. NeurIPS 2023Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions
26. NeurIPS 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
27. NeurIPS 2023Counterfactually Comparing Abstaining Classifiers
28. NeurIPS 2023Deep Non-line-of-sight Imaging from Under-scanning Measurements
29. NeurIPS 2023Detecting hidden confounding in observational data using multiple environments
30. NeurIPS 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
31. NeurIPS 2023Do Not Marginalize Mechanisms, Rather Consolidate!
32. NeurIPS 2023Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
33. NeurIPS 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
34. NeurIPS 2023Estimating Causal Effects Identifiable from a Combination of Observations and Experiments
35. NeurIPS 2023Fair Adaptive Experiments
36. NeurIPS 2023Flow Matching for Scalable Simulation-Based Inference
37. NeurIPS 2023GAN You See Me? Enhanced Data Reconstruction Attacks against Split Inference
38. NeurIPS 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
39. NeurIPS 2023High Precision Causal Model Evaluation with Conditional Randomization
40. NeurIPS 2023IDEA: An Invariant Perspective for Efficient Domain Adaptive Image Retrieval
41. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
42. NeurIPS 2023Implicit Variational Inference for High-Dimensional Posteriors
43. NeurIPS 2023Inference for Gaussian Processes with Matern Covariogram on Compact Riemannian Manifolds
44. NeurIPS 2023Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective
45. NeurIPS 2023Learning non-Markovian Decision-Making from State-only Sequences
46. NeurIPS 2023Learning Nonparametric Latent Causal Graphs with Unknown Interventions
47. NeurIPS 2023Learning Rate Free Sampling in Constrained Domains
48. NeurIPS 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecification
49. NeurIPS 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
50. NeurIPS 2023Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference

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.