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

84 papers, page 1 of 2

1. NeurIPS 2024A Non-parametric Direct Learning Approach to Heterogeneous Treatment Effect Estimation under Unmeasured Confounding
2. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
3. NeurIPS 2024Achievable distributional robustness when the robust risk is only partially identified
4. NeurIPS 2024An effective framework for estimating individualized treatment rules
5. NeurIPS 2024Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
6. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
7. NeurIPS 2024Causal Contrastive Learning for Counterfactual Regression Over Time
8. NeurIPS 2024Causal Dependence Plots
9. NeurIPS 2024Causal Discovery from Event Sequences by Local Cause-Effect Attribution
10. NeurIPS 2024Causal discovery with endogenous context variables
11. NeurIPS 2024Causal Effect Identification in a Sub-Population with Latent Variables
12. NeurIPS 2024Causal Imitation for Markov Decision Processes: a Partial Identification Approach
13. NeurIPS 2024Causal Inference in the Closed-Loop: Marginal Structural Models for Sequential Excursion Effects
14. NeurIPS 2024Causal Temporal Representation Learning with Nonstationary Sparse Transition
15. NeurIPS 2024Causal vs. Anticausal merging of predictors
16. NeurIPS 2024Causal-learn: Causal Discovery in Python
17. NeurIPS 2024Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference
18. NeurIPS 2024Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand
19. NeurIPS 2024Conditional Outcome Equivalence: A Quantile Alternative to CATE
20. NeurIPS 2024Consistency of Neural Causal Partial Identification
21. NeurIPS 2024Covariate Shift Corrected Conditional Randomization Test
22. NeurIPS 2024DeepITE: Designing Variational Graph Autoencoders for Intervention Target Estimation
23. NeurIPS 2024Detecting and Measuring Confounding Using Causal Mechanism Shifts
24. NeurIPS 2024Differentiable Structure Learning with Partial Orders
25. NeurIPS 2024DiffPO: A causal diffusion model for learning distributions of potential outcomes
26. NeurIPS 2024Discovery of the Hidden World with Large Language Models
27. NeurIPS 2024Disentangled Representation Learning in Non-Markovian Causal Systems
28. NeurIPS 2024Do causal predictors generalize better to new domains?
29. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
30. NeurIPS 2024Dynamic Subgroup Identification in Covariate-adjusted Response-adaptive Randomization Experiments
31. NeurIPS 2024Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
32. NeurIPS 2024Efficient Policy Evaluation Across Multiple Different Experimental Datasets
33. NeurIPS 2024Efficiently Learning Significant Fourier Feature Pairs for Statistical Independence Testing
34. NeurIPS 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
35. NeurIPS 2024Exogenous Matching: Learning Good Proposals for Tractable Counterfactual Estimation
36. NeurIPS 2024Fast Proxy Experiment Design for Causal Effect Identification
37. NeurIPS 2024Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
38. NeurIPS 2024From Causal to Concept-Based Representation Learning
39. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
40. NeurIPS 2024Hierarchical and Density-based Causal Clustering
41. NeurIPS 2024Higher-Order Causal Message Passing for Experimentation with Complex Interference
42. NeurIPS 2024Identifiability Analysis of Linear ODE Systems with Hidden Confounders
43. NeurIPS 2024Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
44. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
45. NeurIPS 2024Identifying General Mechanism Shifts in Linear Causal Representations
46. NeurIPS 2024Intervention and Conditioning in Causal Bayesian Networks
47. NeurIPS 2024Interventional Causal Discovery in a Mixture of DAGs
48. NeurIPS 2024Learning Discrete Concepts in Latent Hierarchical Models
49. NeurIPS 2024Learning Discrete Latent Variable Structures with Tensor Rank Conditions
50. NeurIPS 2024Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity

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.