CLeaR 2025

58 papers

Sander Beckers

Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery

Rebecca J. Herman, Jonas Wahl, Urmi Ninad, Jakob Runge

Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between Agents

Rhys Peter Matthew Howard, Lars Kunze

Relational Object-Centric Actor-Critic

Leonid Anatolievich Ugadiarov, Vitaliy Vorobyov, Aleksandr Panov

Causal Identification in Time Series Models

Erik L Jahn, Karthik Karnik, Leonard Schulman

On Measuring Intrinsic Causal Attributions in Deep Neural Networks

Saptarshi Saha, Dhruv Vansraj Rathore, Soumadeep Saha, DAVID DOERMANN, Utpal Garain

Compositional Models for Estimating Causal Effects

Purva Pruthi, David Jensen

Temporal Inverse Probability Weighting for Causal Discovery in Controlled Before–After Studies: Discovering ADEs in Generics

Aubrey Barnard, Peggy L. Peissig, David Page

Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models

Yi Han, Joseph Ramsey, Peter Spirtes

Counterfactual Token Generation in Large Language Models

Ivi Chatzi, Nina L. Corvelo Benz, Eleni Straitouri, Stratis Tsirtsis, Manuel Gomez-Rodriguez

Causal drivers of dynamic networks

Melania Lembo, Ester Riccardi, Veronica Vinciotti, Ernst C. Wit

Multi-Domain Causal Discovery in Bijective Causal Models

Kasra Jalaldoust, Saber Salehkaleybar, Negar Kiyavash

Your Assumed DAG is Wrong And Here’s How To Deal With It

Kirtan Padh, Zhufeng Li, Cecilia Casolo, Niki Kilbertus

Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding

Fateme Jamshidi, Sina Akbari, Negar Kiyavash

MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical Systems

Elise Zhang, François Mirallès, Raphaël Rousseau-Rizzi, Arnaud Zinflou, Di Wu

Counterfactual explanability of black-box prediction models

Zijun Gao, Qingyuan Zhao

Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding

Yufeng Wu, Rohit Bhattacharya

Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets

Christine W. Bang, Vanessa Didelez

Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation

Melanie F. Pradier, Javier Gonzalez

Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set

Davin Choo, Chandler Squires, Arnab Bhattacharyya, David Sontag

Local Interference: Removing Interference Bias in Semi-Parametric Causal Models

Michael O’Riordan, Ciarán Mark Gilligan-Lee

Controlling for discrete unmeasured confounding in nonlinear causal models

Patrick Burauel, Frederick Eberhardt, Michel Besserve

AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal Discovery

Daniel Kornai, Ricardo Silva, Nikolaos Nikolaou

Inducing Causal Structure Applied to Glucose Prediction for T1DM Patients

Ana Esponera, Giovanni Cinà

Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs

Fucheng Warren Zhu, Connor Thomas Jerzak, Adel Daoud

The Causal-Effect Score in Data Management

Felipe Azúa, Leopoldo Bertossi

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad K"ording, Karen Sachs

Omitted Labels Induce Nontransitive Paradoxes in Causality

Bijan Mazaheri, Siddharth Jain, Matthew Cook, Jehoshua Bruck

Counterfactual Influence in Markov Decision Processes

Milad Kazemi, Jessica Lally, Ekaterina Tishchenko, Hana Chockler, Nicola Paoletti

The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions

Jack Kuipers, Giusi Moffa

Disparate Effect Of Missing Mediators On Transportability of Causal Effects

Vishwali Mhasawade, Rumi Chunara

Transfer learning in latent contextual bandits with covariate shift through causal transportability

Mingwei Deng, Ville Kyrki, Dominik Baumann

Aligning Graphical and Functional Causal Abstractions

Willem Schooltink, Fabio Massimo Zennaro

Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding

Jose Peña

Cross-validating causal discovery via Leave-One-Variable-Out

Daniela Schkoda, Philipp Michael Faller, Dominik Janzing, Patrick Blöbaum

Exact discovery is polynomial for certain sparse causal Bayesian networks

Felix Leopoldo Rios, Giusi Moffa, Jack Kuipers

Interpretable Neural Causal Models with TRAM-DAGs

Beate Sick, Oliver Dürr

Score matching through the roof: linear, nonlinear, and latent variables causal discovery

Francesco Montagna, Philipp Michael Faller, Patrick Blöbaum, Elke Kirschbaum, Francesco Locatello

The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data

Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf H Roohani, Pascal Notin, Artemy Bakulin

Actual Causation and Nondeterministic Causal Models

Sander Beckers

Robust Multi-view Co-expression Network Inference

Teodora Pandeva, Martijs Johannes Jonker, Leendert Hamoen, Joris Mooij, Patrick Forré

Encode-Decoder-based GAN for Estimating Counterfactual Outcomes under Sequential Selection Bias and Combinatorial Explosion

Yoshiyuki Norimatsu, Masaaki Imaizumi

Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation

Oana-Iuliana Popescu, Andreas Gerhardus, Martin Rabel, Jakob Runge

Automatic debiasing of neural networks via moment-constrained learning

Christian L. Hines, Oliver J. Hines

Beyond Single-Feature Importance with ICECREAM

Michael Oesterle, Patrick Blöbaum, Atalanti A. Mastakouri, Elke Kirschbaum

Fair Clustering: A Causal Perspective

Fritz Bayer, Drago Plecko, Niko Beerenwinkel, Jack Kuipers

Shapley-PC: Constraint-based Causal Structure Learning with a Shapley Inspired Framework

Fabrizio Russo, Francesca Toni

Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes

Seungeon Lee, Nina L. Corvelo Benz, Suhas Thejaswi, Manuel Gomez-Rodriguez

Contagion Effect Estimation Using Proximal Embeddings

Zahra Fatemi, Elena Zheleva

Algorithmic causal structure emerging through compression

Liang Wendong, Simon Buchholz, Bernhard Schölkopf

Scalable Causal Structure Learning via Amortized Conditional Independence Testing

James Leiner, Brian Manzo, Aaditya Ramdas, Wesley Tansey

Stabilized Inverse Probability Weighting via Isotonic Calibration

Lars van der Laan, Ziming Lin, Marco Carone, Alex Luedtke

Combining Causal Models for More Accurate Abstractions of Neural Networks

Theodora-Mara Pîslar, Sara Magliacane, Atticus Geiger

The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference

Johan de Aguas, Sebastian Krumscheid, Johan Pensar, Guido Biele

An Asymmetric Independence Model for Causal Discovery on Path Spaces

Georg Manten, Cecilia Casolo, Søren Wengel Mogensen, Niki Kilbertus

Causal Bandits without Graph Learning

Mikhail Konobeev, Jalal Etesami, Negar Kiyavash

Causal reasoning in difference graphs

Charles K. Assaad

Algorithmic syntactic causal identification

Dhurim Cakiqi, Max A Little