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