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 6 of 8

251. ICLR 2024Robust agents learn causal world models
252. ICLR 2024Structural Estimation of Partially Observed Linear Non-Gaussian Acyclic Model: A Practical Approach with Identifiability
253. ICML 2024Adaptive Online Experimental Design for Causal Discovery
254. ICML 2024Automated Statistical Model Discovery with Language Models
255. ICML 2024Bivariate Causal Discovery using Bayesian Model Selection
256. ICML 2024Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
257. ICML 2024Causal Discovery via Conditional Independence Testing with Proxy Variables
258. ICML 2024Causal Discovery with Fewer Conditional Independence Tests
259. ICML 2024Causal Inference from Competing Treatments
260. ICML 2024Causal Representation Learning Made Identifiable by Grouping of Observational Variables
261. ICML 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
262. ICML 2024Collaborative Heterogeneous Causal Inference Beyond Meta-analysis
263. ICML 2024Discovering Mixtures of Structural Causal Models from Time Series Data
264. ICML 2024Enforcing Constraints in RNA Secondary Structure Predictions: A Post-Processing Framework Based on the Assignment Problem
265. ICML 2024Foundations of Testing for Finite-Sample Causal Discovery
266. ICML 2024How Transformers Learn Causal Structure with Gradient Descent
267. ICML 2024Intersectional Unfairness Discovery
268. ICML 2024Learning Causal Dynamics Models in Object-Oriented Environments
269. ICML 2024Learning Causal Relations from Subsampled Time Series with Two Time-Slices
270. ICML 2024Local Causal Structure Learning in the Presence of Latent Variables
271. ICML 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference
272. ICML 2024On Positivity Condition for Causal Inference
273. ICML 2024On the Recoverability of Causal Relations from Temporally Aggregated I.I.D. Data
274. ICML 2024On the sample complexity of conditional independence testing with Von Mises estimator with application to causal discovery
275. ICML 2024Optimal Kernel Choice for Score Function-based Causal Discovery
276. ICML 2024Optimal Transport for Structure Learning Under Missing Data
277. ICML 2024Reducing Balancing Error for Causal Inference via Optimal Transport
278. ICML 2024Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency
279. ICML 2024Score-Based Causal Discovery of Latent Variable Causal Models
280. ICML 2024Stable Differentiable Causal Discovery
281. ICML 2024StableMask: Refining Causal Masking in Decoder-only Transformer
282. ICML 2024Tight Partial Identification of Causal Effects with Marginal Distribution of Unmeasured Confounders
283. ICML 2024Transport of Algebraic Structure to Latent Embeddings
284. ICML 2024Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
285. NeurIPS 2024A Simple yet Scalable Granger Causal Structural Learning Approach for Topological Event Sequences
286. NeurIPS 2024Amortized Active Causal Induction with Deep Reinforcement Learning
287. NeurIPS 2024Causal Bandits for Linear Structural Equation Models
288. NeurIPS 2024Causal Discovery from Event Sequences by Local Cause-Effect Attribution
289. NeurIPS 2024Causal discovery with endogenous context variables
290. NeurIPS 2024Causal vs. Anticausal merging of predictors
291. NeurIPS 2024Causal-learn: Causal Discovery in Python
292. NeurIPS 2024CausalStock: Deep End-to-end Causal Discovery for News-driven Multi-stock Movement Prediction
293. NeurIPS 2024ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions
294. NeurIPS 2024Clustering in Causal Attention Masking
295. NeurIPS 2024Differentiable Structure Learning with Partial Orders
296. NeurIPS 2024Discovery of the Hidden World with Large Language Models
297. NeurIPS 2024Do causal predictors generalize better to new domains?
298. NeurIPS 2024Do Finetti: On Causal Effects for Exchangeable Data
299. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
300. NeurIPS 2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise 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.