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 8 of 8

351. ICML 2023Proximal Causal Learning of Conditional Average Treatment Effects
352. ICML 2023Sequential Monte Carlo Learning for Time Series Structure Discovery
353. NeurIPS 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
354. NeurIPS 2023Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders
355. NeurIPS 2023Assumption violations in causal discovery and the robustness of score matching
356. NeurIPS 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
357. NeurIPS 2023Bayesian Active Causal Discovery with Multi-Fidelity Experiments
358. NeurIPS 2023Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data
359. NeurIPS 2023Causal discovery from observational and interventional data across multiple environments
360. NeurIPS 2023Causal Discovery from Subsampled Time Series with Proxy Variables
361. NeurIPS 2023Causal Discovery in Semi-Stationary Time Series
362. NeurIPS 2023Causal Effect Identification in Uncertain Causal Networks
363. NeurIPS 2023Causal Imitability Under Context-Specific Independence Relations
364. NeurIPS 2023Causal Interpretation of Self-Attention in Pre-Trained Transformers
365. NeurIPS 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing
366. NeurIPS 2023Characterization and Learning of Causal Graphs with Small Conditioning Sets
367. NeurIPS 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data
368. NeurIPS 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventions
369. NeurIPS 2023Identification of Nonlinear Latent Hierarchical Models
370. NeurIPS 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
371. NeurIPS 2023Learning Causal Models under Independent Changes
372. NeurIPS 2023Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
373. NeurIPS 2023Meek Separators and Their Applications in Targeted Causal Discovery
374. NeurIPS 2023Rotating Features for Object Discovery
375. NeurIPS 2023Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling
376. NeurIPS 2023Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
377. NeurIPS 2023Towards Distribution-Agnostic Generalized Category Discovery
378. NeurIPS 2023Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
379. NeurIPS 2023Unpaired Multi-Domain Causal Representation Learning

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.