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

379 papers, page 1 of 8

1. CVPR 2026A Polynomial Chaos Framework for Causal Discovery in Nonlinear Uncertain
2. CVPR 2026CHAL Causal-guided Hierarchical Anomaly-aware Learning for Moving Infrared Small Target
3. CVPR 2026GeneVAR Causal MeanFlow for Autoregressive Gene-to-WSI Tile Synthesis
4. CVPR 2026Learning by Analogy A Causal Framework for Compositional Generalization
5. CVPR 2026PlanaReLoc Camera Relocalization in 3D Planar Primitives via Region-Based Structure
6. CVPR 2026Seeing Through the Shift Causality-Inspired Robust Generalized Category Discovery
7. CVPR 2026Stake the Points Structure-Faithful Instance Unlearning
8. ICLR 2026Bures Generalized Category Discovery
9. ICLR 2026CARL: Preserving Causal Structure in Representation Learning
10. ICLR 2026Causal Discovery in the Wild: A Voting-Theoretic Ensemble Approach
11. ICLR 2026Causal Discovery via Quantile Partial Effect
12. ICLR 2026Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks
13. ICLR 2026Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
14. ICLR 2026Coarse-to-Fine Learning of Dynamic Causal Structures
15. ICLR 2026Conditional Independent Component Analysis For Estimating Causal Structure with Latent Variables
16. ICLR 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning
17. ICLR 2026Efficient Ensemble Conditional Independence Test Framework for Causal Discovery
18. ICLR 2026Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
19. ICLR 2026Frequency-Domain Better than Time-Domain for Causal Structure Recovery in Dynamical Systems on Networks
20. ICLR 2026How Transformers Learn Causal Structures In-Context: Explainable Mechanism Meets Theoretical Guarantee
21. ICLR 2026Influence without Confounding: Causal Discovery from Temporal Data with Long-term Carry-over Effects
22. ICLR 2026Learning Dynamic Causal Graphs Under Parametric Uncertainty via Polynomial Chaos Expansions
23. ICLR 2026On Measuring Influence in Avoiding Undesired Future
24. ICLR 2026On the identifiability of causal graphs with multiple environments
25. ICLR 2026Query-Specific Causal Graph Pruning Under Tiered Knowledge
26. ICLR 2026Robust Equation Structure learning with Adaptive Refinement
27. ICLR 2026Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
28. ICLR 2026Some Neural Networks Inherently Preserve Subspace Clustering Structure
29. ICLR 2026Structure Learning from Time-Series Data with Lag-Agnostic Structural Prior
30. ICLR 2026TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
31. ICLR 2026Theoretical Guarantees for Causal Discovery on Large Random Graphs
32. ICLR 2026Topological Causal Effects
33. ICML 2026A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
34. ICML 2026Alleviating Observation Bias via Causal-Invariant Meta-Learning for Unbalanced Incomplete Multi-view Clustering
35. ICML 2026Anti-causal domain generalization: Leveraging unlabeled data
36. ICML 2026Caracal: Causal Architecture via Spectral Mixing
37. ICML 2026Causal Discovery for Irregularly Time Series with Consistency Guarantees
38. ICML 2026Causal discovery for time series with endogenous context variables
39. ICML 2026Causal Fine-Tuning under Latent Confounded Shift
40. ICML 2026Causal Matrix Completion under Multiple Treatments via Mixed Synthetic Nearest Neighbors
41. ICML 2026Causal Modeling of Selection in Evolution
42. ICML 2026Causal Preference Elicitation
43. ICML 2026Causal Structure Learning for Sparse Matrix Fill-in Reduction
44. ICML 2026Causal-EPIG: Causally Aligned Active CATE Estimation
45. ICML 2026CauScale: Neural Causal Discovery at Scale
46. ICML 2026Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations
47. ICML 2026Credibility-Aware Weighting Federated Causal Discovery for Time Series
48. ICML 2026Distributionally Robust Causal Abstractions
49. ICML 2026Do LLMs “Feel”? Emotion Circuits Discovery and Control
50. ICML 2026Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure

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.