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Publication search results
found 40 matches
- 2023
- Hao Zou, Haotian Wang, Renzhe Xu, Bo Li, Jian Pei, Ye Jun Jian, Peng Cui:
Factual Observation Based Heterogeneity Learning for Counterfactual Prediction. CLeaR 2023: 350-370 - Jose M. Peña:
Factorization of the Partial Covariance in Singly-Connected Path Diagrams. CLeaR 2023: 814-849 - Haiying Huang, Adnan Darwiche:
An Algorithm and Complexity Results for Causal Unit Selection. CLeaR 2023: 1-26 - Chi Zhang, Karthika Mohan, Judea Pearl:
Causal Inference under Interference and Model Uncertainty. CLeaR 2023: 371-385 - Ananth Balashankar, Srikanth Jagabathula, Lakshmi Subramanian:
Learning Conditional Granger Causal Temporal Networks. CLeaR 2023: 692-706 - Sander Beckers, Joseph Y. Halpern, Christopher Hitchcock:
Causal Models with Constraints. CLeaR 2023: 866-879 - Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto:
Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios. CLeaR 2023: 243-258 - James Cussens:
Branch-Price-and-Cut for Causal Discovery. CLeaR 2023: 642-661 - Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen:
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. CLeaR 2023: 707-725 - Mário A. T. Figueiredo, Catarina A. Oliveira:
Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model. CLeaR 2023: 122-141 - 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. CLeaR 2023: 880-894 - 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. CLeaR 2023: 791-813 - Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman:
Causal Inference Despite Limited Global Confounding via Mixture Models. CLeaR 2023: 574-601 - Shantanu Gupta, David Childers, Zachary Chase Lipton:
Local Causal Discovery for Estimating Causal Effects. CLeaR 2023: 408-447 - Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal:
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CLeaR 2023: 386-407 - Rhys Howard, Lars Kunze:
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour. CLeaR 2023: 473-498 - Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee:
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition. CLeaR 2023: 448-472 - Connor Thomas Jerzak, Fredrik Daniel Johansson, Adel Daoud:
Image-based Treatment Effect Heterogeneity. CLeaR 2023: 531-552 - Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers:
Backtracking Counterfactuals. CLeaR 2023: 177-196 - 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. CLeaR 2023: 553-573 - Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev:
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. CLeaR 2023: 662-691 - Malte Luttermann, Marcel Wienöbst, Maciej Liskiewicz:
Practical Algorithms for Orientations of Partially Directed Graphical Models. CLeaR 2023: 259-280 - Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu:
Causal Abstraction with Soft Interventions. CLeaR 2023: 68-87 - Søren Wengel Mogensen:
Instrumental Processes Using Integrated Covariances. CLeaR 2023: 620-641 - Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. CLeaR 2023: 726-751 - Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello:
Scalable Causal Discovery with Score Matching. CLeaR 2023: 752-771 - Wojciech Niemiro, Lukasz Rajkowski:
Local Dependence Graphs for Discrete Time Processes. CLeaR 2023: 772-790 - Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus:
Stochastic Causal Programming for Bounding Treatment Effects. CLeaR 2023: 142-176 - Andreas W. M. Sauter, Erman Acar, Vincent François-Lavet:
A Meta-Reinforcement Learning Algorithm for Causal Discovery. CLeaR 2023: 602-619 - Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin P. Bennett:
Influence-Aware Attention for Multivariate Temporal Point Processes. CLeaR 2023: 499-517
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