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

99 papers, page 2 of 2

51. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
52. ICML 2025Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
53. ICML 2025Hierarchical Reinforcement Learning with Targeted Causal Interventions
54. ICML 2025IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
55. ICML 2025Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations
56. ICML 2025Identifying biological perturbation targets through causal differential networks
57. ICML 2025Identifying Causal Direction via Variational Bayesian Compression
58. ICML 2025Isolated Causal Effects of Natural Language
59. ICML 2025Latent Variable Causal Discovery under Selection Bias
60. ICML 2025Local Identifying Causal Relations in the Presence of Latent Variables
61. ICML 2025Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?
62. ICML 2025Permutation-based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Data
63. ICML 2025Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery
64. ICML 2025Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation
65. ICML 2025Strong and Weak Identifiability of Optimization-based Causal Discovery in Non-linear Additive Noise Models
66. NeurIPS 2025Causal Differentiating Concepts: Interpreting LM Behavior via Causal Representation Learning
67. NeurIPS 2025Causal Discovery and Inference through Next-Token Prediction
68. NeurIPS 2025Causal Discovery over Clusters of Variables in Markovian Systems
69. NeurIPS 2025Causal Mixture Models: Characterization and Discovery
70. NeurIPS 2025CausalDynamics: A large‐scale benchmark for structural discovery of dynamical causal models
71. NeurIPS 2025Characterization and Learning of Causal Graphs from Hard Interventions
72. NeurIPS 2025Decoding Causal Structure: End-to-End Mediation Pathways Inference
73. NeurIPS 2025Differentiable Constraint-Based Causal Discovery
74. NeurIPS 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
75. NeurIPS 2025Differentiable Structure Learning and Causal Discovery for General Binary Data
76. NeurIPS 2025Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises
77. NeurIPS 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
78. NeurIPS 2025GLNCD: Graph-Level Novel Category Discovery
79. NeurIPS 2025Identifying Macro Causal Effects in C-DMGs over DMGs
80. NeurIPS 2025Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization
81. NeurIPS 2025It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
82. NeurIPS 2025Less Greedy Equivalence Search
83. NeurIPS 2025Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
84. NeurIPS 2025Meta-D2AG: Causal Graph Learning with Interventional Dynamic Data
85. NeurIPS 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Models
86. NeurIPS 2025Non-Stationary Structural Causal Bandits
87. NeurIPS 2025Novel Class Discovery for Point Cloud Segmentation via Joint Learning of Causal Representation and Reasoning
88. NeurIPS 2025Pattern-Guided Adaptive Prior for Structure Learning
89. NeurIPS 2025Practical Kernel Selection for Kernel-based Conditional Independence Test
90. NeurIPS 2025Quantifying Distributional Invariance in Causal Subgraph for IRM-Free Graph Generalization
91. NeurIPS 2025Revealing Multimodal Causality with Large Language Models
92. NeurIPS 2025Score-informed Neural Operator for Enhancing Ordering-based Causal Discovery
93. NeurIPS 2025SPARTAN: A Sparse Transformer World Model Attending to What Matters
94. NeurIPS 2025Structural Causal Bandits under Markov Equivalence
95. NeurIPS 2025The third pillar of causal analysis? A measurement perspective on causal representations
96. NeurIPS 2025Towards Identifiability of Hierarchical Temporal Causal Representation Learning
97. NeurIPS 2025UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems
98. NeurIPS 2025Universal Causal Inference in a Topos
99. NeurIPS 2025When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discovery

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.