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2nd CLeaR 2023: Tübingen, Germany
- Mihaela van der Schaar, Cheng Zhang, Dominik Janzing:
Conference on Causal Learning and Reasoning, CLeaR 2023, 11-14 April 2023, Amazon Development Center, Tübingen, Germany, April 11-14, 2023. Proceedings of Machine Learning Research 213, PMLR 2023 - Haiying Huang, Adnan Darwiche:
An Algorithm and Complexity Results for Causal Unit Selection. 1-26 - Shiqing Yu, Mathias Drton, Ali Shojaie:
Directed Graphical Models and Causal Discovery for Zero-Inflated Data. 27-67 - Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu:
Causal Abstraction with Soft Interventions. 68-87 - Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei, Widanalage Dhammika Widanage, Theodoros Damoulas:
Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions. 88-121 - Mário A. T. Figueiredo, Catarina A. Oliveira:
Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model. 122-141 - Kirtan Padh
, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus:
Stochastic Causal Programming for Bounding Treatment Effects. 142-176 - Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers:
Backtracking Counterfactuals. 177-196 - Eric V. Strobl, Thomas A. Lasko:
Generalizing Clinical Trials with Convex Hulls. 197-221 - Abhishek Kumar Umrawal:
Leveraging Causal Graphs for Blocking in Randomized Experiments. 222-242 - Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto:
Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios. 243-258 - Malte Luttermann, Marcel Wienöbst, Maciej Liskiewicz:
Practical Algorithms for Orientations of Partially Directed Graphical Models. 259-280 - Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf:
Unsupervised Object Learning via Common Fate. 281-327 - Jonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf:
On the Interventional Kullback-Leibler Divergence. 328-349 - Hao Zou, Haotian Wang, Renzhe Xu, Bo Li, Jian Pei, Ye Jun Jian, Peng Cui:
Factual Observation Based Heterogeneity Learning for Counterfactual Prediction. 350-370 - Chi Zhang, Karthika Mohan, Judea Pearl:
Causal Inference under Interference and Model Uncertainty. 371-385 - Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal:
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? 386-407 - Shantanu Gupta, David Childers, Zachary Chase Lipton:
Local Causal Discovery for Estimating Causal Effects. 408-447 - Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee:
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition. 448-472 - Rhys Howard, Lars Kunze:
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour. 473-498 - Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin P. Bennett:
Influence-Aware Attention for Multivariate Temporal Point Processes. 499-517 - Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey M. Plis:
Causal Learning through Deliberate Undersampling. 518-530 - Connor Thomas Jerzak, Fredrik Daniel Johansson, Adel Daoud:
Image-based Treatment Effect Heterogeneity. 531-552 - Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello:
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. 553-573 - Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman:
Causal Inference Despite Limited Global Confounding via Mixture Models. 574-601 - Andreas W. M. Sauter, Erman Acar, Vincent François-Lavet:
A Meta-Reinforcement Learning Algorithm for Causal Discovery. 602-619 - Søren Wengel Mogensen:
Instrumental Processes Using Integrated Covariances. 620-641 - James Cussens:
Branch-Price-and-Cut for Causal Discovery. 642-661 - Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev:
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. 662-691 - Ananth Balashankar, Srikanth Jagabathula, Lakshmi Subramanian:
Learning Conditional Granger Causal Temporal Networks. 692-706 - Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen:
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. 707-725 - Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. 726-751 - Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. 752-771 - Wojciech Niemiro, Lukasz Rajkowski:
Local Dependence Graphs for Discrete Time Processes. 772-790 - Graham W. Van Goffrier, Lucas Maystre, Ciarán Mark Gilligan-Lee:
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding. 791-813 - Jose M. Peña:
Factorization of the Partial Covariance in Singly-Connected Path Diagrams. 814-849 - Jakob Zeitler, Athanasios Vlontzos, Ciarán Mark Gilligan-Lee:
Non-parametric identifiability and sensitivity analysis of synthetic control models. 850-865 - Sander Beckers, Joseph Y. Halpern, Christopher Hitchcock:
Causal Models with Constraints. 866-879 - Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu:
Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling. 880-894 - Eric V. Strobl, Thomas A. Lasko:
Sample-Specific Root Causal Inference with Latent Variables. 895-915
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