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

36 papers, page 1 of 1

1. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
2. NeurIPS 2024Amortized Active Causal Induction with Deep Reinforcement Learning
3. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
4. NeurIPS 2024Causal Discovery from Event Sequences by Local Cause-Effect Attribution
5. NeurIPS 2024Causal discovery with endogenous context variables
6. NeurIPS 2024Causal vs. Anticausal merging of predictors
7. NeurIPS 2024Causal-learn: Causal Discovery in Python
8. NeurIPS 2024CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction
9. NeurIPS 2024ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions
10. NeurIPS 2024Clustering in Causal Attention Masking
11. NeurIPS 2024Differentiable Structure Learning with Partial Orders
12. NeurIPS 2024Discovery of the Hidden World with Large Language Models
13. NeurIPS 2024Do causal predictors generalize better to new domains?
14. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
15. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
16. NeurIPS 2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Models
17. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
18. NeurIPS 2024Interventional Causal Discovery in a Mixture of DAGs
19. NeurIPS 2024Learning Discrete Concepts in Latent Hierarchical Models
20. NeurIPS 2024Learning Discrete Latent Variable Structures with Tensor Rank Conditions
21. NeurIPS 2024Learning Identifiable Factorized Causal Representations of Cellular Responses
22. NeurIPS 2024Learning Mixtures of Unknown Causal Interventions
23. NeurIPS 2024Learning the Latent Causal Structure for Modeling Label Noise
24. NeurIPS 2024MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
25. NeurIPS 2024On Causal Discovery in the Presence of Deterministic Relations
26. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
27. NeurIPS 2024On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function
28. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
29. NeurIPS 2024Optimal ablation for interpretability
30. NeurIPS 2024Ordering-Based Causal Discovery for Linear and Nonlinear Relations
31. NeurIPS 2024Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
32. NeurIPS 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
33. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
34. NeurIPS 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventions
35. NeurIPS 2024Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
36. NeurIPS 2024WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking

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.