Causal Discovery papers, 2023–2026

Key findings

• Causal Discovery went from 59 papers in 2023 to 139 in 2026, roughly 2.4x in three years.
• NeurIPS publishes the most Causal Discovery work: 150 papers from 2023 to 2026, 40% of the four-venue total.
• The fastest-growing overlap is with General ML · unsupervised and semisupervised learning: papers tagged with both rose from 3 in 2023 to 10 in 2026.
• Among the 5 topics under Causal and Fairness, it ranks #1 by 2026 paper count; the fastest grower in the group, Bias Mitigation, is up 1.5x versus 2.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
• ICML 2026A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
• ICLR 2026Bures Generalized Category Discovery
• CVPR 2026Learning by Analogy A Causal Framework for Compositional Generalization
• NeurIPS 2025Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
• ICML 2025Bayesian Active Learning for Bivariate Causal Discovery
• ICLR 2025A Conditional Independence Test in the Presence of Discretization
• CVPR 2025Hyperbolic Category Discovery
• NeurIPS 2024Causal Bandits for Linear Structural Equation Models
• ICML 2024Adaptive Online Experimental Design for Causal Discovery
• ICLR 2024A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
• CVPR 2024Contrastive Mean-Shift Learning for Generalized Category Discovery
• NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
• ICML 2023Active causal structure learning with advice
• ICLR 2023Boosting Causal Discovery via Adaptive Sample Reweighting
• CVPR 2023Intrinsic Physical Concepts Discovery With Object-Centric Predictive Models
• NeurIPS 2026Act, Validate, Adapt: Closing the Causal Discovery-Control Loop
• ICML 2026Causal Discovery for Irregularly Time Series with Consistency Guarantees
• ICLR 2026Causal Discovery in the Wild: A Voting-Theoretic Ensemble Approach
• CVPR 2026PlanaReLoc Camera Relocalization in 3D Planar Primitives via Region-Based Structure

All Causal Discovery papers

82 papers, page 1 of 2

1. CVPR 2024Contrastive Mean-Shift Learning for Generalized Category Discovery
2. ICLR 2024A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
3. ICLR 2024Can Large Language Models Infer Causation from Correlation?
4. ICLR 2024Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning
5. ICLR 2024Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
6. ICLR 2024CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
7. ICLR 2024CoLiDE: Concomitant Linear DAG Estimation
8. ICLR 2024Federated Causal Discovery from Heterogeneous Data
9. ICLR 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
10. ICLR 2024MetaPhysiCa: Improving OOD Robustness in Physics-informed Machine Learning
11. ICLR 2024Neural structure learning with stochastic differential equations
12. ICLR 2024ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
13. ICLR 2024Robust agents learn causal world models
14. ICLR 2024Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability
15. ICML 2024Adaptive Online Experimental Design for Causal Discovery
16. ICML 2024Automated Statistical Model Discovery with Language Models
17. ICML 2024Bivariate Causal Discovery using Bayesian Model Selection
18. ICML 2024Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
19. ICML 2024Causal Discovery via Conditional Independence Testing with Proxy Variables
20. ICML 2024Causal Discovery with Fewer Conditional Independence Tests
21. ICML 2024Causal Inference from Competing Treatments
22. ICML 2024Causal Representation Learning Made Identifiable by Grouping of Observational Variables
23. ICML 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
24. ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
25. ICML 2024Discovering Mixtures of Structural Causal Models from Time Series Data
26. ICML 2024Enforcing Constraints in RNA Secondary Structure Predictions: A Post-Processing Framework Based on the Assignment Problem
27. ICML 2024Foundations of Testing for Finite-Sample Causal Discovery
28. ICML 2024How Transformers Learn Causal Structure with Gradient Descent
29. ICML 2024Intersectional Unfairness Discovery
30. ICML 2024Learning Causal Dynamics Models in Object-Oriented Environments
31. ICML 2024Learning Causal Relations from Subsampled Time Series with Two Time-Slices
32. ICML 2024Local Causal Structure Learning in the Presence of Latent Variables
33. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
34. ICML 2024On Positivity Condition for Causal Inference
35. ICML 2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
36. ICML 2024On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
37. ICML 2024Optimal Kernel Choice for Score Function-based Causal Discovery
38. ICML 2024Optimal Transport for Structure Learning Under Missing Data
39. ICML 2024Reducing Balancing Error for Causal Inference via Optimal Transport
40. ICML 2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
41. ICML 2024Score-Based Causal Discovery of Latent Variable Causal Models
42. ICML 2024Stable Differentiable Causal Discovery
43. ICML 2024StableMask: Refining Causal Masking in Decoder-only Transformer
44. ICML 2024Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
45. ICML 2024Transport of Algebraic Structure to Latent Embeddings
46. ICML 2024Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
47. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
48. NeurIPS 2024Amortized Active Causal Induction with Deep Reinforcement Learning
49. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
50. NeurIPS 2024Causal Discovery from Event Sequences by Local Cause-Effect Attribution

FAQ

How many Causal Discovery papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

379 in total: 59 in 2023, 82 in 2024, 99 in 2025, 139 in 2026.

How many Causal Discovery papers are at NeurIPS 2026?

NeurIPS 2026 has 53 Causal Discovery papers, versus 34 the year before.

How many Causal Discovery papers are at ICLR 2026?

ICLR 2026 has 25 Causal Discovery papers, versus 27 the year before.

How many Causal Discovery papers are at ICML 2026?

ICML 2026 has 54 Causal Discovery papers, versus 31 the year before.

How many Causal Discovery papers are at CVPR 2026?

CVPR 2026 has 7 Causal Discovery papers, versus 7 the year before.

Which conference publishes the most Causal Discovery papers?

NeurIPS, with 150 papers from 2023 to 2026.