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

59 papers, page 1 of 2

1. CVPR 2023Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery
2. CVPR 2023Dynamic Conceptional Contrastive Learning for Generalized Category Discovery
3. CVPR 2023Intrinsic Physical Concepts Discovery With Object-Centric Predictive Models
4. CVPR 2023Level-S$^2$fM: Structure From Motion on Neural Level Set of Implicit Surfaces
5. CVPR 2023On-the-Fly Category Discovery
6. CVPR 2023PartMix: Regularization Strategy To Learn Part Discovery for Visible-Infrared Person Re-Identification
7. ICLR 2023Boosting Causal Discovery via Adaptive Sample Reweighting
8. ICLR 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
9. ICLR 2023Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
10. ICLR 2023CUTS: Neural Causal Discovery from Irregular Time-Series Data
11. ICLR 2023DAG Learning on the Permutahedron
12. ICLR 2023Diffusion Models for Causal Discovery via Topological Ordering
13. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
14. ICLR 2023Improving the imputation of missing data with Markov Blanket discovery
15. ICLR 2023Learning to Induce Causal Structure
16. ICLR 2023Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise
17. ICML 2023Active causal structure learning with advice
18. ICML 2023Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
19. ICML 2023Behavior Contrastive Learning for Unsupervised Skill Discovery
20. ICML 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
21. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
22. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
23. ICML 2023Collaborative Causal Inference with Fair Incentives
24. ICML 2023Covariate balancing using the integral probability metric for causal inference
25. ICML 2023Differentiable Multi-Target Causal Bayesian Experimental Design
26. ICML 2023From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
27. ICML 2023Interventional Causal Representation Learning
28. ICML 2023Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
29. ICML 2023Nonlinear Causal Discovery with Latent Confounders
30. ICML 2023On Data Manifolds Entailed by Structural Causal Models
31. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
32. ICML 2023Sequential Monte Carlo Learning for Time Series Structure Discovery
33. NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
34. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
35. NeurIPS 2023Assumption violations in causal discovery and the robustness of score matching
36. NeurIPS 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
37. NeurIPS 2023Bayesian Active Causal Discovery with Multi-Fidelity Experiments
38. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
39. NeurIPS 2023Causal discovery from observational and interventional data across multiple environments
40. NeurIPS 2023Causal Discovery from Subsampled Time Series with Proxy Variables
41. NeurIPS 2023Causal Discovery in Semi-Stationary Time Series
42. NeurIPS 2023Causal Effect Identification in Uncertain Causal Networks
43. NeurIPS 2023Causal Imitability Under Context-Specific Independence Relations
44. NeurIPS 2023Causal Interpretation of Self-Attention in Pre-Trained Transformers
45. NeurIPS 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
46. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets
47. NeurIPS 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
48. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
49. NeurIPS 2023Identification of Nonlinear Latent Hierarchical Models
50. NeurIPS 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

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.