CLeaR 2025

58 papers

Actual Causation and Nondeterministic Causal Models Sander Beckers

AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal Discovery Daniel Kornai, Ricardo Silva, Nikolaos Nikolaou

Algorithmic Causal Structure Emerging Through Compression Liang Wendong, Simon Buchholz, Bernhard Schölkopf

Algorithmic Syntactic Causal Identification Dhurim Cakiqi, Max A Little

Aligning Graphical and Functional Causal Abstractions Willem Schooltink, Fabio Massimo Zennaro

An Asymmetric Independence Model for Causal Discovery on Path Spaces Georg Manten, Cecilia Casolo, Søren Wengel Mogensen, Niki Kilbertus

Automatic Debiasing of Neural Networks via Moment-Constrained Learning Christian L. Hines, Oliver J. Hines

Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect Estimation Melanie F. Pradier, Javier González

Beyond Single-Feature Importance with ICECREAM Michael Oesterle, Patrick Blöbaum, Atalanti A. Mastakouri, Elke Kirschbaum

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

Causal Bandits Without Graph Learning Mikhail Konobeev, Jalal Etesami, Negar Kiyavash

Causal Drivers of Dynamic Networks Melania Lembo, Ester Riccardi, Veronica Vinciotti, Ernst C. Wit

Causal Identification in Time Series Models Erik L Jahn, Karthik Karnik, Leonard Schulman

Causal Reasoning in Difference Graphs Charles K. Assaad

Combining Causal Models for More Accurate Abstractions of Neural Networks Theodora-Mara Pîslar, Sara Magliacane, Atticus Geiger

Compositional Models for Estimating Causal Effects Purva Pruthi, David Jensen

Constraint-Based Causal Discovery with Tiered Background Knowledge and Latent Variables in Single or Overlapping Datasets Christine W. Bang, Vanessa Didelez

Contagion Effect Estimation Using Proximal Embeddings Zahra Fatemi, Elena Zheleva

Controlling for Discrete Unmeasured Confounding in Nonlinear Causal Models Patrick Burauel, Frederick Eberhardt, Michel Besserve

Counterfactual Explanability of Black-Box Prediction Models Zijun Gao, Qingyuan Zhao

Counterfactual Influence in Markov Decision Processes Milad Kazemi, Jessica Lally, Ekaterina Tishchenko, Hana Chockler, Nicola Paoletti

Counterfactual Token Generation in Large Language Models Ivi Chatzi, Nina L. Corvelo Benz, Eleni Straitouri, Stratis Tsirtsis, Manuel Gomez-Rodriguez

Cross-Validating Causal Discovery via Leave-One-Variable-Out Daniela Schkoda, Philipp Michael Faller, Dominik Janzing, Patrick Blöbaum

Disparate Effect of Missing Mediators on Transportability of Causal Effects Vishwali Mhasawade, Rumi Chunara

Encode-Decoder-Based GAN for Estimating Counterfactual Outcomes Under Sequential Selection Bias and Combinatorial Explosion Yoshiyuki Norimatsu, Masaaki Imaizumi

Exact Discovery Is Polynomial for Certain Sparse Causal Bayesian Networks Felix Leopoldo Rios, Giusi Moffa, Jack Kuipers

Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between Agents Rhys Peter Matthew Howard, Lars Kunze

Fair Clustering: A Causal Perspective Fritz Bayer, Drago Plečko, Niko Beerenwinkel, Jack Kuipers

Inducing Causal Structure Applied to Glucose Prediction for T1DM Patients Ana Esponera, Giovanni Cinà

Interpretable Neural Causal Models with TRAM-DAGs Beate Sick, Oliver Dürr

Local Interference: Removing Interference Bias in Semi-Parametric Causal Models Michael O’Riordan, Ciarán Mark Gilligan-Lee

Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes Seungeon Lee, Nina L. Corvelo Benz, Suhas Thejaswi, Manuel Gomez-Rodriguez

Multi-Domain Causal Discovery in Bijective Causal Models Kasra Jalaldoust, Saber Salehkaleybar, 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, Benoit Boulet

Network Causal Effect Estimation in Graphical Models of Contagion and Latent Confounding Yufeng Wu, Rohit Bhattacharya

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

Nondeterministic Causal Models Sander Beckers

Omitted Labels Induce Nontransitive Paradoxes in Causality Bijan Mazaheri, Siddharth Jain, Matthew Cook, Jehoshua Bruck

On Measuring Intrinsic Causal Attributions in Deep Neural Networks Saptarshi Saha, Dhruv Vansraj Rathore, Soumadeep Saha, David Doermann, Utpal Garain

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

Probably Approximately Correct High-Dimensional Causal Effect Estimation Given a Valid Adjustment Set Davin Choo, Chandler Squires, Arnab Bhattacharyya, David Sontag

Relational Object-Centric Actor-Critic Leonid Anatolievich Ugadiarov, Vitaliy Vorobyov, Aleksandr Panov

Robust Multi-View Co-Expression Network Inference Teodora Pandeva, Martijs Johannes Jonker, Leendert Hamoen, Joris Mooij, Patrick Forré

Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confounding Fateme Jamshidi, Sina Akbari, Negar Kiyavash

Scalable Causal Structure Learning via Amortized Conditional Independence Testing James Leiner, Brian Manzo, Aaditya Ramdas, Wesley Tansey

Score Matching Through the Roof: Linear, Nonlinear, and Latent Variables Causal Discovery Francesco Montagna, Philipp Michael Faller, Patrick Blöbaum, Elke Kirschbaum, Francesco Locatello

Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical Models Yi Han, Joseph Ramsey, Peter Spirtes

Shapley-PC: Constraint-Based Causal Structure Learning with a Shapley Inspired Framework Fabrizio Russo, Francesca Toni

Stabilized Inverse Probability Weighting via Isotonic Calibration Lars Laan, Ziming Lin, Marco Carone, Alex Luedtke

Temporal Inverse Probability Weighting for Causal Discovery in Controlled Before–After Studies: Discovering ADEs in Generics Aubrey Barnard, Peggy L. Peissig, David Page

The Causal-Effect Score in Data Management Felipe Azúa, Leopoldo Bertossi

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, Dariusz Brzezinski, Kaiwen Deng, Yuanfang Guan, Justin Hong, Michael Ibrahim, Wojciech Kotlowski, Marcin Kowiel, Panagiotis Misiakos, Achille Nazaret, Markus Püschel, Chris Wendler, Arash Mehrjou, Patrick Schwab

The Interventional Bayesian Gaussian Equivalent Score for Bayesian Causal Inference with Unknown Soft Interventions Jack Kuipers, Giusi Moffa

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications Philippe Brouillard, Chandler Squires, Jonas Wahl, Konrad K"ording, Karen Sachs, Alexandre Drouin, Dhanya Sridhar

The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference Johan Aguas, Sebastian Krumscheid, Johan Pensar, Guido Biele

Transfer Learning in Latent Contextual Bandits with Covariate Shift Through Causal Transportability Mingwei Deng, Ville Kyrki, Dominik Baumann

Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery Rebecca J. Herman, Jonas Wahl, Urmi Ninad, Jakob Runge

Your Assumed DAG Is Wrong and Here’s How to Deal with It Kirtan Padh, Zhufeng Li, Cecilia Casolo, Niki Kilbertus