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 2024Bi-Causal: Group Activity Recognition via Bidirectional Causality
2. ICLR 2024Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps
3. ICLR 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Data
4. ICLR 2024Causally Aligned Curriculum Learning
5. ICLR 2024Debiased Collaborative Filtering with Kernel-Based Causal Balancing
6. ICLR 2024Learning in reverse causal strategic environments with ramifications on two sided markets
7. ICLR 2024Treatment Effects Estimation By Uniform Transformer
8. ICML 2024A Sparsity Principle for Partially Observable Causal Representation Learning
9. ICML 2024Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
10. ICML 2024Causal Effect Identification in LiNGAM Models with Latent Confounders
11. ICML 2024Causal Inference from Competing Treatments
12. ICML 2024Causal-IQA: Towards the Generalization of Image Quality Assessment Based on Causal Inference
13. ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
14. ICML 2024Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
15. ICML 2024How Transformers Learn Causal Structure with Gradient Descent
16. ICML 2024Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
17. ICML 2024Multiply-Robust Causal Change Attribution
18. ICML 2024On Positivity Condition for Causal Inference
19. ICML 2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
20. ICML 2024Position: The Causal Revolution Needs Scientific Pragmatism
21. ICML 2024Prediction-powered Generalization of Causal Inferences
22. ICML 2024Reducing Balancing Error for Causal Inference via Optimal Transport
23. ICML 2024StableMask: Refining Causal Masking in Decoder-only Transformer
24. ICML 2024Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
25. NeurIPS 2024A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding
26. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
27. NeurIPS 2024Achievable distributional robustness when the robust risk is only partially identified
28. NeurIPS 2024An effective framework for estimating individualized treatment rules
29. NeurIPS 2024Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
30. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
31. NeurIPS 2024Causal Contrastive Learning for Counterfactual Regression Over Time
32. NeurIPS 2024Causal Dependence Plots
33. NeurIPS 2024Causal Discovery from Event Sequences by Local Cause-Effect Attribution
34. NeurIPS 2024Causal discovery with endogenous context variables
35. NeurIPS 2024Causal Effect Identification in a Sub-Population with Latent Variables
36. NeurIPS 2024Causal Imitation for Markov Decision Processes: a Partial Identification Approach
37. NeurIPS 2024Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects
38. NeurIPS 2024Causal Temporal Representation Learning with Nonstationary Sparse Transition
39. NeurIPS 2024Causal vs. Anticausal merging of predictors
40. NeurIPS 2024Causal-learn: Causal Discovery in Python
41. NeurIPS 2024Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
42. NeurIPS 2024Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
43. NeurIPS 2024Conditional Outcome Equivalence: A Quantile Alternative to CATE
44. NeurIPS 2024Consistency of Neural Causal Partial Identification
45. NeurIPS 2024Covariate Shift Corrected Conditional Randomization Test
46. NeurIPS 2024DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation
47. NeurIPS 2024Detecting and Measuring Confounding Using Causal Mechanism Shifts
48. NeurIPS 2024Differentiable Structure Learning with Partial Orders
49. NeurIPS 2024DiffPO: A causal diffusion model for learning distributions of potential outcomes
50. NeurIPS 2024Discovery of the Hidden World with Large Language Models

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.