NeurIPS 2025PastAI for scienceCausality
NeurIPS 2025 Workshop on CauScien: Uncovering Causality in Science
NeurIPS 2025 Workshop CauScien
- Submission deadline
- Sep 1, 2025, 12:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (46)
Fetched from OpenReview (v2) on 2026-06-10.
A Causal Formulation of Spike-Wave Duality
Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective
Applying Time-Series Causal Discovery to Understand Algal Bloom Mechanisms
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
Can LLMs Propose Instrumental Variables for Causal Reasoning?
Capturing Semantic Correctness for Causal Reasoning Evaluation via Symbolic Verification
Carryover detection in switchback experimentation
CAST: Causal Modeling of Time-Varying Treatment Effects on Head and Neck Cancer
Causal AI Scientist: Facilitating Causal Data Science with Large Language Models
Causal Machine Learning for Sustainable Agriculture
Causal Representation Learning from Multimodal EHRs under Non-Random Modality Missingness
Causal Representation Meets Stochastic Modeling under Generic Geometry
Causal Scissor: root cause discovery via the measure of edge cuts in graphs
CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
CauSciBench: Assessing LLM Causal Reasoning for Scientific Research
CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data
Confounding is a Pervasive Problem in Real World Recommender Systems
Cost-Aware Interpolation of Soft Interventions: Blend of Propensity, Target Law, and Product of Experts
Counterfactual NMR: Benchmarking Minimal Spectral Interventions for Interpretable Structure Elucidation
CUVET: A Partitioning Approach for Continuous Treatment Assignment At Scale
Data Decomposition beyond Splitting for Causal Estimation
Disentangling Misreporting from Genuine Adaptation in Strategic Settings: A Causal Approach
Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks
Dual-Latent Generative Causal Structure Learning with Causal Annealing
Dynamic causal discovery in Alzheimer’s disease through latent pseudotime modelling
Efficient Greedy Equivalence Search for Non-Score-Equivalent Criteria using Sampling
From Prediction to Causal Interpretation: A DML Case Study in Financial Economics
How Effective is Your Rebuttal? Identifying Causal Models from the OpenReview System
How reliable are treatment effects in clinical trials with dropout?
Improving precision of A/B experiments using trigger intensity
Inductive Biases for Disentangled Representation Learning with Correlated Treatment--Nuisance Factors
Instrumental Variable Representation Learning under Confounded Covariates
Learning Causal Gene Relationships in Biological Pathways with Graph Attention Networks (GATs)
Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis
LongSurv: Bridging Short-Term Data and Causal Priors for Longitudinal Survival Modeling
MIIC-SR: From Complex Data to Structural Causal Models
Not All Splits Are Equal: Rethinking Attribute Generalization Across Unrelated Categories
On Double Robustness in Double Machine Learning
One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequences
Realizing LLMs’ Causal Potential Requires Science-Grounded, Novel Benchmarks
Recovering Causal Features for Instrumental Variable Regression with Contrastive Learning
Searching for actual causes: Approximate algorithms with adjustable precision
Structure learning without context-specific ground truths: a case study in chronic low-dose radiation exposure in human cells
Towards Causal Understanding of Urban Air Pollution: Mechanistic Models under Sparse Sensing
Transformer Is Inherently a Causal Learner
Using causal abstractions to accelerate decision-making in complex bandit problems