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Workshop Sat, Dec 6, 2025 • 8:00 AM – 5:00 PM PST

CauScien: Uncovering Causality in Science

Dingling Yao ⋅ Jiaqi Zhang ⋅ Piersilvio De Bartolomeis ⋅ Ying Jin ⋅ Alexander D'Amour ⋅ Caroline Uhler ⋅ Kun Zhang ⋅ Francesco Locatello
Project Page [ OpenReview] 

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Schedule

Timezone: America/Los_Angeles
8:15 AM
Opening Remarks
Francesco Locatello
11:00 AM
Poster Session 1
→ CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data
Gerrit Großmann ⋅ Sumantrak Mukherjee ⋅ Sebastian Vollmer
11:00 AM
How reliable are treatment effects in clinical trials with dropout?
Yuxin Wang ⋅ Dennis Frauen ⋅ Jonas Schweisthal ⋅ Stefan Feuerriegel
→ Inductive Biases for Disentangled Representation Learning with Correlated Treatment--Nuisance Factors
Luka Kovacevic ⋅ Sara Jane Dunn ⋅ Hagen Triendl ⋅ Lindsay Edwards ⋅ Sach Mukherjee ⋅ John Whittaker ⋅ Thomas Gaudelet
→ Causal Scissor: root cause discovery via the measure of edge cuts in graphs
Jingi Ju ⋅ Mincheol Shin ⋅ Minwoo Kim ⋅ Giwoong Lee ⋅ Jiseung Ahn ⋅ Jeongyeol Choe
→ MIIC-SR: From Complex Data to Structural Causal Models
Nadir Sella ⋅ Adam Perbost ⋅ Louis VERNY
→ Transformer Is Inherently a Causal Learner
Xinyue Wang ⋅ Zhiguang Wang ⋅ Biwei Huang
→ CUVET: A Partitioning Approach for Continuous Treatment Assignment At Scale
Artem Betlei ⋅ Mariia Vladimirova ⋅ Victor Girou ⋅ Thibaud Rahier
→ Applying Time-Series Causal Discovery to Understand Algal Bloom Mechanisms
Ayush Prasad ⋅ Snehal Verma ⋅ Mohammad Aatish Khan
→ Instrumental Variable Representation Learning under Confounded Covariates
Jungsoo Kim ⋅ Kwonho Kim ⋅ Inwoo Hwang ⋅ Sanghack Lee
→ Using causal abstractions to accelerate decision-making in complex bandit problems
Joel Dyer ⋅ Nicholas Bishop ⋅ Anisoara Calinescu ⋅ Michael Wooldridge ⋅ Fabio Massimo Zennaro
→ Confounding is a Pervasive Problem in Real World Recommender Systems
Alexander Merkov ⋅ David Rohde ⋅ Benjamin Heymann ⋅ Alexandre Gilotte
→ Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification
Paul He ⋅ Yinya Huang ⋅ Mrinmaya Sachan ⋅ Zhijing Jin
→ On Double Robustness in Double Machine Learning
Simon Valentin ⋅ Gianluca Detommaso ⋅ Yikuan Li ⋅ Manfred Opper ⋅ Michael Brueckner
11:00 AM
CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
Benjamin Herdeanu ⋅ Juan Nathaniel ⋅ Carla Roesch ⋅ Jatan Buch ⋅ Gregor Ramien ⋅ Johannes Haux ⋅ Pierre Gentine
→ Dynamic causal discovery in Alzheimer’s disease through latent pseudotime modelling
Natalia Glazman ⋅ Jyoti Mangal ⋅ Pedro Borges ⋅ M. Jorge Cardoso
→ CAST: Causal Modeling of Time-Varying Treatment Effects on Head and Neck Cancer
Everest Yang ⋅ Ria Vasishtha ⋅ Luqman Dad ⋅ Lisa Kachnic ⋅ Andrew Hope ⋅ Eric Wang ⋅ Xiao Wu ⋅ Yading Yuan ⋅ David Brenner ⋅ Igor Shuryak
→ Carryover detection in switchback experimentation
Paul Missault ⋅ Lorenzo Masoero
→ Data Decomposition beyond Splitting for Causal Estimation
Xuelin Yang ⋅ Dhruv Singal ⋅ Rina Friedberg ⋅ Niloy Biswas
→ Learning General Causal Structures with Hidden Dynamic Process for Climate Analysis
Minghao Fu ⋅ Biwei Huang ⋅ Zijian Li ⋅ Yujia Zheng ⋅ Ignavier Ng ⋅ Guangyi Chen ⋅ Yingyao Hu ⋅ Kun Zhang
→ A Causal Formulation of Spike-Wave Duality
Kasra Jalaldoust ⋅ Erfan Zabeh
→ Realizing LLMs’ Causal Potential Requires Science-Grounded, Novel Benchmarks
Ashutosh Srivastava ⋅ Lokesh N ⋅ Gautam Jajoo ⋅ Aniket Vashishtha ⋅ Parameswari Krishnamurthy ⋅ Amit Sharma
→ Improving precision of A/B experiments using trigger intensity
Tanmoy Das ⋅ Dohyeon Lee ⋅ Arnab Sinha
11:00 AM
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
Nikita Dhawan ⋅ Daniel Shen ⋅ Leonardo Cotta ⋅ Chris Maddison
→ Can LLMs Propose Instrumental Variables for Causal Reasoning?
Ivaxi Sheth ⋅ Zhijing Jin ⋅ Bryan Wilder ⋅ Dominik Janzing ⋅ Mario Fritz
→ How Effective is Your Rebuttal? Identifying Causal Models from the OpenReview System
Longkang Li ⋅ Ibrahim Aldarmaki ⋅ Minghao Fu ⋅ Wong Kang ⋅ Yunlong Deng ⋅ Qiang Huang ⋅ Jing Yang ⋅ Jin Tian ⋅ Guangyi Chen ⋅ Kun Zhang
→ Searching for actual causes: Approximate algorithms with adjustable precision
Samuel Reyd ⋅ Ada Diaconescu ⋅ Jean-Louis Dessalles
→ CauSciBench: Assessing LLM Causal Reasoning for Scientific Research
Sawal Acharya ⋅ Terry Jingchen Zhang ⋅ Andrew Kim ⋅ Anahita Haghighat ⋅ Xianlin Sun ⋅ Rahul Babu Shrestha ⋅ Maximilian Mordig ⋅ Furkan Danisman ⋅ Clijo Jose ⋅ Yahang Qi ⋅ Pepijn Cobben ⋅ Bernhard Schölkopf ⋅ Mrinmaya Sachan ⋅ Zhijing Jin
→ Causal AI Scientist: Facilitating Causal Data Science with Large Language Models
Vishal Verma ⋅ Sawal Acharya ⋅ Devansh Bhardwaj ⋅ Samuel Simko ⋅ Yongjin Yang ⋅ Anahita Haghighat ⋅ Dominik Janzing ⋅ Mrinmaya Sachan ⋅ Bernhard Schölkopf ⋅ Zhijing Jin
→ Causal Machine Learning for Sustainable Agriculture
Vasileios Sitokonstantinou ⋅ Emiliano Diaz ⋅ Jordi Cerda-Bautista ⋅ Maria Piles Guillem ⋅ Ioannis Athanasiadis ⋅ Ilias Tsoumas ⋅ Gustau Camps-Valls
→ Not All Splits Are Equal: Rethinking Attribute Generalization Across Unrelated Categories
Firca Nicolae ⋅ Elena Burceanu ⋅ Antonio Barbalau ⋅ Dan Oneata
→ Efficient Greedy Equivalence Search for Non-Score-Equivalent Criteria using Sampling
Rafailia Chatzianastasiou ⋅ Osman Mian ⋅ Jilles Vreeken
→ Learning Causal Gene Relationships in Biological Pathways with Graph Attention Networks (GATs)
Gavin Wong ⋅ Ping Ho ⋅ Ivan Yeung ⋅ Ka Chun Cheung ⋅ Simon See
→ LongSurv: Bridging Short-Term Data and Causal Priors for Longitudinal Survival Modeling
Shivaram Velayutham ⋅ Anuva Kapoor ⋅ Kangan Maria ⋅ Preetha Balasubramanian ⋅ Bhanu Duggal ⋅ Mona Duggal ⋅ Balaraman Ravindran ⋅ Lakshminarayanan Subramanian
→ Estimating Misreporting in the Presence of Genuine Modification: A Causal Perspective
Dylan Zapzalka ⋅ Trenton Chang ⋅ Lindsay Warrenburg ⋅ Sae-Hwan Park ⋅ Daniel Shenfeld ⋅ Ravi Parikh ⋅ Jenna Wiens ⋅ Maggie Makar
12:30 PM
Lunch Break
3:00 PM
Panel Discussion
Jason Hartford ⋅ Nathan Kallus ⋅ Mihaela van der Schaar ⋅ Riccardo Cadei ⋅ Elizabeth Tipton
4:50 PM
Closing Remarks
Dingling Yao ⋅ Jiaqi Zhang ⋅ Piersilvio De Bartolomeis