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 7 of 8

301. NeurIPS 2024Identifying Causal Effects Under Functional Dependencies
302. NeurIPS 2024Interventional Causal Discovery in a Mixture of DAGs
303. NeurIPS 2024Learning Discrete Concepts in Latent Hierarchical Models
304. NeurIPS 2024Learning Discrete Latent Variable Structures with Tensor Rank Conditions
305. NeurIPS 2024Learning Identifiable Factorized Causal Representations of Cellular Responses
306. NeurIPS 2024Learning Mixtures of Unknown Causal Interventions
307. NeurIPS 2024Learning the Latent Causal Structure for Modeling Label Noise
308. NeurIPS 2024MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning
309. NeurIPS 2024On Causal Discovery in the Presence of Deterministic Relations
310. NeurIPS 2024On the Complexity of Identification in Linear Structural Causal Models
311. NeurIPS 2024On the Identifiability of Poisson Branching Structural Causal Model Using Probability Generating Function
312. NeurIPS 2024On the Parameter Identifiability of Partially Observed Linear Causal Models
313. NeurIPS 2024Optimal ablation for interpretability
314. NeurIPS 2024Ordering-Based Causal Discovery for Linear and Nonlinear Relations
315. NeurIPS 2024Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
316. NeurIPS 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMs
317. NeurIPS 2024Sample Complexity of Interventional Causal Representation Learning
318. NeurIPS 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventions
319. NeurIPS 2024Tangent Space Causal Inference: Leveraging Vector Fields for Causal Discovery in Dynamical Systems
320. NeurIPS 2024WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking
321. CVPR 2023Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery
322. CVPR 2023Dynamic Conceptional Contrastive Learning for Generalized Category Discovery
323. CVPR 2023Intrinsic Physical Concepts Discovery With Object-Centric Predictive Models
324. CVPR 2023Level-S$^2$fM: Structure From Motion on Neural Level Set of Implicit Surfaces
325. CVPR 2023On-the-Fly Category Discovery
326. CVPR 2023PartMix: Regularization Strategy To Learn Part Discovery for Visible-Infrared Person Re-Identification
327. ICLR 2023Boosting Causal Discovery via Adaptive Sample Reweighting
328. ICLR 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
329. ICLR 2023Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
330. ICLR 2023CUTS: Neural Causal Discovery from Irregular Time-Series Data
331. ICLR 2023DAG Learning on the Permutahedron
332. ICLR 2023Diffusion Models for Causal Discovery via Topological Ordering
333. ICLR 2023GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints
334. ICLR 2023Improving the imputation of missing data with Markov Blanket discovery
335. ICLR 2023Learning to Induce Causal Structure
336. ICLR 2023Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise
337. ICML 2023Active causal structure learning with advice
338. ICML 2023Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
339. ICML 2023Behavior Contrastive Learning for Unsupervised Skill Discovery
340. ICML 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulants
341. ICML 2023Causal Strategic Classification: A Tale of Two Shifts
342. ICML 2023Causal Structure Learning for Latent Intervened Non-stationary Data
343. ICML 2023Collaborative Causal Inference with Fair Incentives
344. ICML 2023Covariate balancing using the integral probability metric for causal inference
345. ICML 2023Differentiable Multi-Target Causal Bayesian Experimental Design
346. ICML 2023From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
347. ICML 2023Interventional Causal Representation Learning
348. ICML 2023Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies
349. ICML 2023Nonlinear Causal Discovery with Latent Confounders
350. ICML 2023On Data Manifolds Entailed by Structural Causal Models

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.