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 4 of 8

151. ICLR 2025Analytic DAG Constraints for Differentiable DAG Learning
152. ICLR 2025Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension ability
153. ICLR 2025Causal Discovery via Bayesian Optimization
154. ICLR 2025Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference
155. ICLR 2025CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series
156. ICLR 2025Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm
157. ICLR 2025Differentiable Causal Discovery for Latent Hierarchical Causal Models
158. ICLR 2025DyCAST: Learning Dynamic Causal Structure from Time Series
159. ICLR 2025Efficient and Trustworthy Causal Discovery with Latent Variables and Complex Relations
160. ICLR 2025Extendable and Iterative Structure Learning Strategy for Bayesian Networks
161. ICLR 2025Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning
162. ICLR 2025Linear SCM Identification in the Presence of Confounders and Gaussian Noise
163. ICLR 2025Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates
164. ICLR 2025Robust Root Cause Diagnosis using In-Distribution Interventions
165. ICLR 2025Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery
166. ICLR 2025Selective Induction Heads: How Transformers Select Causal Structures in Context
167. ICLR 2025Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
168. ICLR 2025Standardizing Structural Causal Models
169. ICLR 2025THE ROBUSTNESS OF DIFFERENTIABLE CAUSAL DISCOVERY IN MISSPECIFIED SCENARIOS
170. ICLR 2025Towards Empowerment Gain through Causal Structure Learning in Model-Based Reinforcement Learning
171. ICLR 2025Unifying Causal Representation Learning with the Invariance Principle
172. ICLR 2025When Selection Meets Intervention: Additional Complexities in Causal Discovery
173. ICLR 2025Which Tasks Should Be Compressed Together? A Causal Discovery Approach for Efficient Multi-Task Representation Compression
174. ICML 2025A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
175. ICML 2025An Efficient Search-and-Score Algorithm for Ancestral Graphs using Multivariate Information Scores for Complex Non-linear and Categorical Data
176. ICML 2025Arrow: Accelerator for Time Series Causal Discovery with Time Weaving
177. ICML 2025Bayesian Active Learning for Bivariate Causal Discovery
178. ICML 2025Bivariate Causal Discovery with Proxy Variables: Integral Solving and Beyond
179. ICML 2025Causal Abstraction Inference under Lossy Representations
180. ICML 2025Causal Abstraction Learning based on the Semantic Embedding Principle
181. ICML 2025Causal Discovery from Conditionally Stationary Time Series
182. ICML 2025Continuous Bayesian Model Selection for Multivariate Causal Discovery
183. ICML 2025Differentiable Structure Learning with Ancestral Constraints
184. ICML 2025Discovering Latent Causal Graphs from Spatiotemporal Data
185. ICML 2025Distinguishing Cause from Effect with Causal Velocity Models
186. ICML 2025Double Machine Learning for Causal Inference under Shared-State Interference
187. ICML 2025Explicit Discovery of Nonlinear Symmetries from Dynamic Data
188. ICML 2025Extracting Rare Dependence Patterns via Adaptive Sample Reweighting
189. ICML 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
190. ICML 2025Federated Causal Structure Learning with Non-identical Variable Sets
191. ICML 2025Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
192. ICML 2025Hierarchical Reinforcement Learning with Targeted Causal Interventions
193. ICML 2025IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
194. ICML 2025Identification of Latent Confounders via Investigating the Tensor Ranks of the Nonlinear Observations
195. ICML 2025Identifying biological perturbation targets through causal differential networks
196. ICML 2025Identifying Causal Direction via Variational Bayesian Compression
197. ICML 2025Isolated Causal Effects of Natural Language
198. ICML 2025Latent Variable Causal Discovery under Selection Bias
199. ICML 2025Local Identifying Causal Relations in the Presence of Latent Variables
200. ICML 2025Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?

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.