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

75 papers, page 2 of 2

51. ICLR 2025When Selection Meets Intervention: Additional Complexities in Causal Discovery
52. ICLR 2025Which Tasks Should Be Compressed Together? A Causal Discovery Approach for Efficient Multi-Task Representation Compression
53. ICLR 2024A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
54. ICLR 2024Can Large Language Models Infer Causation from Correlation?
55. ICLR 2024Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning
56. ICLR 2024Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions
57. ICLR 2024CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
58. ICLR 2024CoLiDE: Concomitant Linear DAG Estimation
59. ICLR 2024Federated Causal Discovery from Heterogeneous Data
60. ICLR 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
61. ICLR 2024MetaPhysiCa: Improving OOD Robustness in Physics-informed Machine Learning
62. ICLR 2024Neural structure learning with stochastic differential equations
63. ICLR 2024ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference
64. ICLR 2024Robust agents learn causal world models
65. ICLR 2024Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability
66. ICLR 2023Boosting Causal Discovery via Adaptive Sample Reweighting
67. ICLR 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
68. ICLR 2023Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
69. ICLR 2023CUTS: Neural Causal Discovery from Irregular Time-Series Data
70. ICLR 2023DAG Learning on the Permutahedron
71. ICLR 2023Diffusion Models for Causal Discovery via Topological Ordering
72. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
73. ICLR 2023Improving the imputation of missing data with Markov Blanket discovery
74. ICLR 2023Learning to Induce Causal Structure
75. ICLR 2023Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise

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.