Table of Contents

• Algorithmic syntactic causal identificationDhurim Cakiqi, Max A. Little. 1-14 [doi]
• Causal reasoning in difference graphsCharles K. Assaad. 15-30 [doi]
• Causal Bandits without Graph LearningMikhail Konobeev, Jalal Etesami, Negar Kiyavash. 31-63 [doi]
• An Asymmetric Independence Model for Causal Discovery on Path SpacesGeorg Manten, Cecilia Casolo, Søren Wengel Mogensen, Niki Kilbertus. 64-89 [doi]
• The Probability of Tiered Benefit: Partial Identification with Robust and Stable InferenceJohan de Aguas, Sebastian Krumscheid, Johan Pensar, Guido Biele. 90-113 [doi]
• Combining Causal Models for More Accurate Abstractions of Neural NetworksTheodora-Mara Pîslar, Sara Magliacane, Atticus Geiger. 114-138 [doi]
• Stabilized Inverse Probability Weighting via Isotonic CalibrationLars van der Laan, Ziming Lin, Marco Carone, Alex Luedtke. 139-173 [doi]
• Scalable Causal Structure Learning via Amortized Conditional Independence TestingJames Leiner, Brian Manzo, Aaditya Ramdas, Wesley Tansey. 174-200 [doi]
• Algorithmic causal structure emerging through compressionWendong Liang, Simon Buchholz, Bernhard Schölkopf. 201-242 [doi]
• Contagion Effect Estimation Using Proximal EmbeddingsZahra Fatemi, Elena Zheleva. 243-259 [doi]
• Matchings, Predictions and Counterfactual Harm in Refugee Resettlement ProcessesSeungEon Lee 0001, Nina L. Corvelo Benz, Suhas Thejaswi, Manuel Gomez-Rodriguez. 260-291 [doi]
• Shapley-PC: Constraint-based Causal Structure Learning with a Shapley Inspired FrameworkFabrizio Russo 0002, Francesca Toni. 292-339 [doi]
• Fair Clustering: A Causal PerspectiveFritz M. Bayer, Drago Plecko, Niko Beerenwinkel, Jack Kuipers. 340-358 [doi]
• Beyond Single-Feature Importance with ICECREAMMichael Oesterle, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Elke Kirschbaum. 359-389 [doi]
• Automatic debiasing of neural networks via moment-constrained learningChristian L. Hines, Oliver J. Hines. 390-405 [doi]
• Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical EvaluationOana-Iuliana Popescu, Andreas Gerhardus, Martin Rabel, Jakob Runge. 406-450 [doi]
• Encode-Decoder-based GAN for Estimating Counterfactual Outcomes under Sequential Selection Bias and Combinatorial ExplosionYoshiyuki Norimatsu, Masaaki Imaizumi. 451-489 [doi]
• Robust Multi-view Co-expression Network InferenceTeodora Pandeva, Martijs Jonker, Leendert Hamoen, Joris M. Mooij, Patrick Forré. 490-513 [doi]
• Actual Causation and Nondeterministic Causal ModelsSander Beckers. 514-532 [doi]
• The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation dataMathieu Chevalley, Jacob Sackett-Sanders, Yusuf H. Roohani, Pascal Notin, Artemy Bakulin, Dariusz Brzezinski, Kaiwen Deng, Yuanfang Guan, Justin Hong, Michael Ibrahim 0003, Wojciech Kotlowski, Marcin Kowiel, Panagiotis Misiakos, Achille Nazaret, Markus Püschel, Chris Wendler, Arash Mehrjou, Patrick Schwab. 533-551 [doi]
• Score matching through the roof: linear, nonlinear, and latent variables causal discoveryFrancesco Montagna, Philipp Michael Faller, Patrick Blöbaum, Elke Kirschbaum, Francesco Locatello. 552-605 [doi]
• Interpretable Neural Causal Models with TRAM-DAGsBeate Sick, Oliver Dürr. 606-630 [doi]
• Exact discovery is polynomial for certain sparse causal Bayesian networksFelix Leopoldo Rios, Giusi Moffa, Jack Kuipers. 631-658 [doi]
• Cross-validating causal discovery via Leave-One-Variable-OutDaniela Schkoda, Philipp Michael Faller, Dominik Janzing, Patrick Blöbaum. 659-692 [doi]
• Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR ConfoundingJosé Peña. 693-703 [doi]
• Aligning Graphical and Functional Causal AbstractionsWillem Schooltink, Fabio Massimo Zennaro. 704-730 [doi]
• Transfer learning in latent contextual bandits with covariate shift through causal transportabilityMingwei Deng, Ville Kyrki, Dominik Baumann. 731-756 [doi]
• Disparate Effect Of Missing Mediators On Transportability of Causal EffectsVishwali Mhasawade, Rumi Chunara. 757-771 [doi]
• The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionsJack Kuipers, Giusi Moffa. 772-791 [doi]
• Counterfactual Influence in Markov Decision ProcessesMilad Kazemi, Jessica Lally, Ekaterina Tishchenko, Hana Chockler, Nicola Paoletti. 792-817 [doi]
• Omitted Labels Induce Nontransitive Paradoxes in CausalityBijan Mazaheri, Siddharth Jain, Matthew Cook 0001, Jehoshua Bruck. 818-833 [doi]
• The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World ApplicationsPhilippe Brouillard, Chandler Squires, Jonas Wahl, Konrad K, Karen Sachs, Alexandre Drouin, Dhanya Sridhar. 834-873 [doi]
• The Causal-Effect Score in Data ManagementFelipe Azúa, Leopoldo Bertossi. 874-893 [doi]
• Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTsFucheng Warren Zhu, Connor Thomas Jerzak, Adel Daoud. 894-919 [doi]
• Inducing Causal Structure Applied to Glucose Prediction for T1DM PatientsAna Esponera, Giovanni Cinà. 920-946 [doi]
• AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal DiscoveryDaniel Kornai, Ricardo Silva, Nikolaos Nikolaou. 947-990 [doi]
• Controlling for discrete unmeasured confounding in nonlinear causal modelsPatrick Burauel, Frederick Eberhardt, Michel Besserve. 991-1015 [doi]
• Local Interference: Removing Interference Bias in Semi-Parametric Causal ModelsMichael O'Riordan, Ciarán Mark Gilligan-Lee. 1016-1031 [doi]
• Probably approximately correct high-dimensional causal effect estimation given a valid adjustment setDavin Choo, Chandler Squires, Arnab Bhattacharyya 0001, David Sontag. 1032-1085 [doi]
• Beyond Flatland: A Geometric Take on Matching Methods for Treatment Effect EstimationMelanie F. Pradier, Javier González. 1086-1115 [doi]
• Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasetsChristine W. Bang, Vanessa Didelez. 1116-1146 [doi]
• Network Causal Effect Estimation In Graphical Models Of Contagion And Latent ConfoundingYufeng Wu, Rohit Bhattacharya. 1147-1173 [doi]
• Counterfactual explanability of black-box prediction modelsZijun Gao, Qingyuan Zhao. 1174 [doi]
• MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in Dynamical SystemsJiuqi Elise Zhang, François Mirallès, Raphaël Rousseau-Rizzi, Arnaud Zinflou, Di Wu 0044, Benoit Boulet. 1175-1216 [doi]
• Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden ConfoundingFateme Jamshidi, Sina Akbari, Negar Kiyavash. 1217-1238 [doi]
• Your Assumed DAG is Wrong And Here's How To Deal With ItKirtan Padh, Zhufeng Li, Cecilia Casolo, Niki Kilbertus. 1239-1267 [doi]
• Multi-Domain Causal Discovery in Bijective Causal ModelsKasra Jalaldoust, Saber Salehkaleybar, Negar Kiyavash. 1268-1289 [doi]
• Causal drivers of dynamic networksMelania Lembo, Ester Riccardi, Veronica Vinciotti, Ernst C. Wit. 1290 [doi]
• Counterfactual Token Generation in Large Language ModelsIvi Chatzi, Nina L. Corvelo Benz, Eleni Straitouri, Stratis Tsirtsis, Manuel Gomez-Rodriguez. 1291-1315 [doi]
• Selecting Accurate Subgraphical Models from Possibly Inaccurate Graphical ModelsYi Han, Joseph D. Ramsey, Peter Spirtes. 1316-1346 [doi]
• Temporal Inverse Probability Weighting for Causal Discovery in Controlled Before-After Studies: Discovering ADEs in GenericsAubrey Barnard, Peggy L. Peissig, David Page. 1347-1364 [doi]
• Compositional Models for Estimating Causal EffectsPurva Pruthi, David D. Jensen. 1365-1404 [doi]
• On Measuring Intrinsic Causal Attributions in Deep Neural NetworksSaptarshi Saha, Dhruv Vansraj Rathore, Soumadeep Saha, David S. Doermann, Utpal Garain. 1405-1434 [doi]
• Causal Identification in Time Series ModelsErik Jahn, Karthik Karnik, Leonard J. Schulman. 1435-1449 [doi]
• Relational Object-Centric Actor-CriticLeonid Ugadiarov, Vitaliy Vorobyov, Aleksandr Panov. 1450-1476 [doi]
• Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between AgentsRhys Howard, Lars Kunze. 1477-1505 [doi]
• Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal DiscoveryRebecca Herman, Jonas Wahl, Urmi Ninad, Jakob Runge. 1506-1531 [doi]
• Nondeterministic Causal ModelsSander Beckers. 1532-1554 [doi]