Table of Contents

• An Algorithm and Complexity Results for Causal Unit SelectionHaiying Huang 0002, Adnan Darwiche. 1-26 [doi]
• Directed Graphical Models and Causal Discovery for Zero-Inflated DataShiqing Yu, Mathias Drton, Ali Shojaie. 27-67 [doi]
• Causal Abstraction with Soft InterventionsRiccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu. 68-87 [doi]
• Jointly Learning Consistent Causal Abstractions Over Multiple Interventional DistributionsFabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei, Widanalage Dhammika Widanage, Theodoros Damoulas. 88-121 [doi]
• Distinguishing Cause from Effect on Categorical Data: The Uniform Channel ModelMário A. T. Figueiredo, Catarina A. Oliveira. 122-141 [doi]
• Stochastic Causal Programming for Bounding Treatment EffectsKirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus. 142-176 [doi]
• Backtracking CounterfactualsJulius von Kügelgen, Abdirisak Mohamed, Sander Beckers. 177-196 [doi]
• Generalizing Clinical Trials with Convex HullsEric Strobl, Thomas A Lasko. 197-221 [doi]
• Leveraging Causal Graphs for Blocking in Randomized ExperimentsAbhishek Kumar Umrawal. 222-242 [doi]
• Enhancing Causal Discovery from Robot Sensor Data in Dynamic ScenariosLuca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto. 243-258 [doi]
• Practical Algorithms for Orientations of Partially Directed Graphical ModelsMalte Luttermann, Marcel Wienöbst, Maciej Liskiewicz. 259-280 [doi]
• Unsupervised Object Learning via Common FateMatthias Tangemann, Steffen Schneider 0001, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf. 281-327 [doi]
• On the Interventional Kullback-Leibler DivergenceJonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya 0001, Bernhard Schölkopf. 328-349 [doi]
• Factual Observation Based Heterogeneity Learning for Counterfactual PredictionHao Zou 0001, Haotian Wang 0001, Renzhe Xu, Bo Li, Jian Pei, Ye Jun Jian, Peng Cui 0001. 350-370 [doi]
• Causal Inference under Interference and Model UncertaintyChi Zhang 0016, Karthika Mohan, Judea Pearl. 371-385 [doi]
• Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal. 386-407 [doi]
• Local Causal Discovery for Estimating Causal EffectsShantanu Gupta, David Childers, Zachary Chase Lipton. 408-447 [doi]
• On Discovery of Local Independence over Continuous Variables via Neural Contextual DecompositionInwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee. 448-472 [doi]
• Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver BehaviourRhys Howard, Lars Kunze. 473-498 [doi]
• Influence-Aware Attention for Multivariate Temporal Point ProcessesXiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin P. Bennett. 499-517 [doi]
• Causal Learning through Deliberate UndersamplingKseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey M. Plis. 518-530 [doi]
• Image-based Treatment Effect HeterogeneityConnor Thomas Jerzak, Fredrik Daniel Johansson, Adel Daoud. 531-552 [doi]
• Causal Triplet: An Open Challenge for Intervention-centric Causal Representation LearningYuejiang Liu, Alexandre Alahi, Chris Russell 0001, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello. 553-573 [doi]
• Causal Inference Despite Limited Global Confounding via Mixture ModelsSpencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman. 574-601 [doi]
• A Meta-Reinforcement Learning Algorithm for Causal DiscoveryAndreas W. M. Sauter, Erman Acar, Vincent François-Lavet. 602-619 [doi]
• Instrumental Processes Using Integrated CovariancesSøren Wengel Mogensen. 620-641 [doi]
• Branch-Price-and-Cut for Causal DiscoveryJames Cussens. 642-661 [doi]
• Learning Causal Representations of Single Cells via Sparse Mechanism Shift ModelingRomain Lopez, Natasa Tagasovska, Stephen Ra, KyungHyun Cho, Jonathan K. Pritchard, Aviv Regev. 662-691 [doi]
• Learning Conditional Granger Causal Temporal NetworksAnanth Balashankar, Srikanth Jagabathula, Lakshmi Subramanian. 692-706 [doi]
• Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent ConfoundingMirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen. 707-725 [doi]
• Causal Discovery with Score Matching on Additive Models with Arbitrary NoiseFrancesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang 0001, Francesco Locatello. 726-751 [doi]
• Scalable Causal Discovery with Score MatchingFrancesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang 0001, Francesco Locatello. 752-771 [doi]
• Local Dependence Graphs for Discrete Time ProcessesWojciech Niemiro, Lukasz Rajkowski. 772-790 [doi]
• Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confoundingGraham Van Goffrier, Lucas Maystre, Ciarán Mark Gilligan-Lee. 791-813 [doi]
• Factorization of the Partial Covariance in Singly-Connected Path DiagramsJose M. Peña 0001. 814-849 [doi]
• Non-parametric identifiability and sensitivity analysis of synthetic control modelsJakob Zeitler, Athanasios Vlontzos, Ciarán Mark Gilligan-Lee. 850-865 [doi]
• Causal Models with ConstraintsSander Beckers, Joseph Y. Halpern, Christopher Hitchcock. 866-879 [doi]
• Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time ModelingDaigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu. 880-894 [doi]
• Sample-Specific Root Causal Inference with Latent VariablesEric V. Strobl, Thomas A. Lasko. 895-915 [doi]