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

139 papers, page 2 of 3

51. ICML 2026Evaluating Bivariate Causal Statements Based on Mutual Compatibility
52. ICML 2026Formalizing and Falsifying Causal Pathways of Rare Events
53. ICML 2026Function-Valued Causal Influence in Nonlinear Time Series
54. ICML 2026Global Directional Priors with Local Statistical Validation for Scalable Causal Discovery
55. ICML 2026Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control
56. ICML 2026Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
57. ICML 2026Identifiable Nonlinear Differentiable Causal Discovery via Independence and Adaptive Group Sparsity
58. ICML 2026Identifying Partially Observed Causal Models from Heterogeneous/Nonstationary Data
59. ICML 2026Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations
60. ICML 2026Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis
61. ICML 2026Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement
62. ICML 2026Local Covariate Selection for Average Causal Effect Estimation without Pretreatment and Causal Sufficiency Assumptions
63. ICML 2026Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds
64. ICML 2026Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithms
65. ICML 2026On the Identifiability of Poisson Branching Structural Causal Model Under Latent Confounding
66. ICML 2026One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State
67. ICML 2026PACER: Acyclic Causal Discovery from Large-scale Interventional Data
68. ICML 2026PathwayLLM: Explainable Clinical Trajectory Modeling with Structured Pathways for Sepsis Prediction
69. ICML 2026Power-Boosted Granger-Causal Discovery for Large Heterogeneous Panel Data
70. ICML 2026Powerful and Theoretically Guaranteed Independence Testing on Heterogeneous Federated Clients
71. ICML 2026Regret-Based Federated Causal Discovery with Unknown Interventions
72. ICML 2026Ripple Perturbations Through Structure: Likelihood-Constrained Adversarial Attacks on Heterogeneous Tabular Data
73. ICML 2026Robust Causal Discovery in Real-World Time Series with Power-Laws
74. ICML 2026Root Cause Analysis of Failures in Microservices via Bayesian Root Cause Discovery
75. ICML 2026SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
76. ICML 2026Shapley Regularized Neural Granger Causality
77. ICML 2026Skewness-Robust Causal Discovery in Location-Scale Noise Models
78. ICML 2026Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference
79. ICML 2026Spiked-CFR: Causal Representation Learning from LLMs via Wasserstein Projection Pursuit
80. ICML 2026Split Group Knockoffs: Controlling False Discovery Rate in Transformational Group Sparsity
81. ICML 2026Test-Time Learning of Causal Structure from Interventional Data
82. ICML 2026The Fisher Dimension: Instance-Dependent Complexity for Causal Discovery
83. ICML 2026TopoDistill: Distilling Global System Topology for Causal Discovery in Multivariate Time Series
84. ICML 2026Towards Completeness in Causal Discovery from Soft Interventions with Known Targets
85. ICML 2026Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
86. ICML 2026Use What You Know: Causal Foundation Models with Partial Graphs
87. NeurIPS 2026A Characterization of Latent Variable Causal Models Consistent with Observational Data
88. NeurIPS 2026Act, Validate, Adapt: Closing the Causal Discovery-Control Loop
89. NeurIPS 2026AgentCBO: Causal Belief Routing for Bayesian Optimization under Unknown Graphs
90. NeurIPS 2026Attention-Based Pretraining for Unsupervised Amortized Causal Discovery
91. NeurIPS 2026Automated Causal Effect Estimation through Self-Evolving AI
92. NeurIPS 2026Causal Abstractions, Categorically Unified
93. NeurIPS 2026Causal Discovery from Unseen Environments
94. NeurIPS 2026Causal discovery needs explicit epistemic standards
95. NeurIPS 2026Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach
96. NeurIPS 2026Causal Discovery under Time-Varying Delays
97. NeurIPS 2026Causal Discovery via Transformed Low-Rank Quantile Surfaces
98. NeurIPS 2026Causal Discovery with False Positive Error Control
99. NeurIPS 2026Causal Gradient Steering: Exposing Shortcut Gradients by Destroying Causal Signal
100. NeurIPS 2026Causal learning with the invariance principle

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.