CLeaR 2026

53 papers

Kaveh Aryan, Hana Chockler, Mohammad Reza Mousavi

Information-Theoretic Causal Bounds under Unmeasured Confounding

Yonghan Jung, Bogyeong Kang

Causal and Active Learning-Based Counterfactual Chest X-ray Generation for Supporting Clinical Decision-Making in Lung Disease

Yifei Zhu, Greta Mohr, Lei Zhang, Christopher Sainsbury, Feng Dong

Proxy-Guided Measurement Calibration

Saketh Vishnubhatla, Shu Wan, Andre Harrison, Adrienne Raglin, Huan Liu

Sharp Bounds for Treatment Effect Generalization under Outcome Distribution Shift

Amir Asiaee, Samhita Pal, Cole Beck, Jared Davis Huling

Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions

Panayiotis Panayiotou, Ozgur Simsek

Bayesian Causal Discovery Networks for Linear Mixed Data

Moabi Mokhoro, Ioan Gabriel Bucur, Tom Heskes, Jildau Bouwman, Tom Claassen

Intervening to learn and compose causally disentangled representations

Alex Markham, Isaac Hirsch, Jeri A. Chang, Liam Solus, Bryon Aragam

Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem

Turan Orujlu, Christian Gumbsch, Martin V. Butz, Charley M Wu

Causal Discovery for Efficient Offline RL with Factored Action Spaces

Cecilia Ehrlichman, Michael Dykstra, Shengpu Tang, Maggie Makar

Estimation and Inference for Causal Explainability

Weihan Zhang, Zijun Gao

Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding

Keith Barnatchez, Kevin P. Josey, Rachel C. Nethery, Giovanni Parmigiani

Coarsening Causal DAG Models

Francisco Madaleno, Pratik Misra, Alex Markham

IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery

Ivaxi Sheth, Zhijing Jin, Bryan Wilder, Dominik Janzing, Mario Fritz

Beware of Hinges in Proximal Variables Regression: Adjusting for Colliding-Mediators with Nuisance PV

Hubert Marek Drazkowski

Domain Faithfulness through Counterfactually Robust Learning

Ananth Balashankar, Ankit Bhardwaj, Neelabh Madan, Thomas Wies, Lakshmi Subramanian

The Generalised Kernel Covariance Measure

Luca Bergen, Dino Sejdinovic, Vanessa Didelez

Nonparametric Greedy Equivalence Search with Prior-Fitted Networks

Mateusz Gajewski, Mateusz Olko

Causal Foundations of Collective Agency

Frederik Hytting Jørgensen, Sebastian Weichwald, Lewis Hammond

Information-theoretic signatures of causality in Bayesian networks and hypergraphs

Sung En Chiang, Zhaolu Liu, Robert Peach, Mauricio Barahona

Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approach

Tiago Brogueira, Mario A. T. Figueiredo

Cost-Aware Optimized Front-Door Experimental Design

Leopold Mareis, Mathias Drton

Causal Articulation Theory (CAT): Articulating Static and Temporal Causal Models Beyond LLM Rationalizations

Matej Zečević, Devendra Singh Dhami, Kristian Kersting

A causal machine learning framework for pharmacovigilance signal detection in electronic health records: Drug-induced acute kidney injury

Stella Dimitsaki, Corinne Isnard Bagnis, Pantelis Natsiavas, Marie-Christine Jaulent

Abstractions in Causal Models and Game Structures

Sylvia S. Kerkhove, Natasha Alechina, Mehdi Dastani

A Comprehensive Collection of Vignettes for Actual Causation

Christian Odenwald

Composite Graphical Causal Models for Inference on Heterogeneous-Indexed Data

Arne De Temmerman, Mathias Verbeke

Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables

Marc Braun, Jose Peña, Adel Daoud

Learning control variables and instruments for causal analysis in observational data

Nicolas Apfel, Martin Huber, Jannis Kueck, Julia Hatamyar

Smoothing the Landscape: Causal Structure Learning via Diffusion Denoising Objectives

Hao Zhu, Di Zhou, Donna Slonim

Unveiling Causal Calibration: How LLM Scale Influences Statistical Information Interpolation

Markus Englberger, Devendra Singh Dhami

Estimating PATE under positivity violations: SBART+SPL for high-dimensional covariates

Lennard Maßmann

Variance reduction combining pre-experiment and in-experiment data

Zhexiao Lin, Pablo Crespo

Estimating Joint Interventional Distributions from Marginal Interventional Data

Sergio Hernan Garrido Mejia, Elke Kirschbaum, Armin Kekić, Bernhard Schölkopf, Atalanti A. Mastakouri

Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoders

Martin Huber, Jannis Kueck, Mara Mattes

Causal Importance for Physics-Informed Machine Learning

Daniel Fiifi Tawia Hagan, Thomas Mortier, Cas Decancq, Diego G. Miralles

Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

Katherine Avery, Chinmay Pendse, David Jensen

Learning a Spatial Partitioning and its Causal Relations from Temporal Data

Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar

Improving Generative Methods for Causal Evaluation via Simulation-Based Inference

Pracheta Amaranath, Vinitra Muralikrishnan, Amit Sharma, David Jensen

Cross-Sectional Conditional Independence in Stationary Time Series: Graphical Equivalence and Completeness of Collider Separation

Hubert Marek Drazkowski

Retrieving Classes of Causal Orders with Inconsistent Knowledge Bases

Federico Baldo, Simon Ferreira, Charles K. Assaad

Automatic debiased machine learning and sensitivity analysis for sample selection models

Jakob Bjelac, Victor Chernozhukov, Phil-Adrian Klotz, Jannis Kueck, Theresa M. A. Schmitz

Bayesian Hierarchical Invariant Prediction

Francisco Madaleno, Pernille Julie Viuff Sand, Francisco C. Pereira, Sergio Hernan Garrido Mejia

A novel hybrid approach for positive-valued DAG learning

Yao Zhao

DAG Learning from Zero-Inflated Count Data Using Continuous Optimization

Noriaki Sato, Marco Scutari, Shuichi Kawano, Rui Yamaguchi, Seiya Imoto

Causal-ICM: A Data Fusion Framework For Heterogeneous Treatment Effect Estimation With Multi-Task Gaussian Processes

Evangelos Dimitriou, Edwin Fong, Jens Magelund Tarp, Karla DiazOrdaz, Brieuc Lehmann

Valid Inference for Treatment Effects under Multimodal Confounding

Martin Spindler, Philipp Bach, Victor Chernozhukov, Sven Klaassen, Jan Teichert-Kluge

Understanding Task Representations in Neural Networks via Bayesian Ablation

Andrew Joohun Nam, Declan Iain Campbell, Thomas L. Griffiths, Jonathan D. Cohen, Sarah-Jane Leslie

A fine-grained look at causal effects in causal spaces

Junhyung Park, Yuqing Zhou

Disentangling Dynamical Systems: Causal Representation Learning Meets Local Sparse Attention

Markus W. Baumgartner, Anson Lei, Joe Watson, Ingmar Posner

Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effects

Timothée Loranchet, Charles K. Assaad

Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approach

Juraj Bodik

Consistent End-to-End Estimation for Counterfactual Fairness

Yuchen Ma, Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel