default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCID3rd CLeaR 2024: Los Angeles, CA, USA
- Francesco Locatello, Vanessa Didelez:
Causal Learning and Reasoning, 1-3 April 2024, Los Angeles, California, USA. Proceedings of Machine Learning Research 236, PMLR 2024 - David Strieder
, Mathias Drton:
Dual Likelihood for Causal Inference under Structure Uncertainty. 1-17 - Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan, Fred Morstatter:
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding. 18-40 - Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar:
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. 41-70 - Ignavier Ng, Biwei Huang, Kun Zhang:
Structure Learning with Continuous Optimization: A Sober Look and Beyond. 71-105 - Wenhao Lu, Xufeng Zhao, Thilo Fryen, Jae Hee Lee, Mengdi Li, Sven Magg, Stefan Wermter:
Causal State Distillation for Explainable Reinforcement Learning. 106-142 - Alicia Curth, Hoifung Poon, Aditya V. Nori, Javier González:
Cautionary Tales on Synthetic Controls in Survival Analyses. 143-159 - Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah D. Goodman:
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations. 160-187 - Francisco Nunes Ferreira Quialheiro Simoes, Mehdi Dastani, Thijs van Ommen:
Fundamental Properties of Causal Entropy and Information Gain. 188-208 - Martin Rohbeck, Brian Clarke, Katharina Mikulik, Alexandra Pettet, Oliver Stegle, Kai Ueltzhöffer:
Bicycle: Intervention-Based Causal Discovery with Cycles. 209-242 - Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva:
Pragmatic Fairness: Developing Policies with Outcome Disparity Control. 243-264 - Gabriele D'Acunto, Francesco Bonchi, Gianmarco De Francisci Morales, Giovanni Petri:
Extracting the Multiscale Causal Backbone of Brain Dynamics. 265-295 - Davide Talon, Phillip Lippe, Stuart James, Alessio Del Bue, Sara Magliacane:
Towards the Reusability and Compositionality of Causal Representations. 296-324 - Ruta Binkyte, Carlos Pinzón, Szilvia Lestyan, Kangsoo Jung, Héber Hwang Arcolezi, Catuscia Palamidessi:
Causal Discovery Under Local Privacy. 325-383 - Vitória Barin Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent:
On the Identifiability of Quantized Factors. 384-422 - Fateme Jamshidi, Jalal Etesami, Negar Kiyavash:
Confounded Budgeted Causal Bandits. 423-461 - Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini, Theodoros Damoulas:
Causal Optimal Transport of Abstractions. 462-498 - Yang Liu, Marius Hofert:
Implicit and Explicit Policy Constraints for Offline Reinforcement Learning. 499-513 - Philipp Dettling, Mathias Drton, Mladen Kolar:
On the Lasso for Graphical Continuous Lyapunov Models. 514-550 - Philip A. Boeken, Onno Zoeter, Joris M. Mooij:
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift. 551-569 - Urja Pawar, Christian Beder, Ruairi O'Reilly, Donna O'Shea:
On the Impact of Neighbourhood Sampling to Satisfy Sufficiency and Necessity Criteria in Explainable AI. 570-586 - Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Anthony Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia:
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. 587-608 - Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth, Steffen Sonntag:
extttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. 609-642 - Jan Corazza, Hadi Partovi Aria, Daniel Neider, Zhe Xu:
Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment. 643-664 - Andrew Ying:
Causality for Functional Longitudinal Data. 665-687 - Abhishek Dalvi, Neil Ashtekar, Vasant G. Honavar:
Causal Matching using Random Hyperplane Tessellations. 688-702 - Damian Machlanski, Spyridon Samothrakis, Paul Clarke:
Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice. 703-739 - Ricardo Miguel de Oliveira Moreira, Jacopo Bono, Mário Cardoso, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro:
DiConStruct: Causal Concept-based Explanations through Black-Box Distillation. 740-768 - Caterina De Bacco, Yixin Wang, David M. Blei:
A causality-inspired plus-minus model for player evaluation in team sports. 769-792 - Ben Dai, Chunlin Li, Haoran Xue, Wei Pan, Xiaotong Shen:
Inference of nonlinear causal effects with application to TWAS with GWAS summary data. 793-826 - Malte Luttermann, Mattis Hartwig, Tanya Braun, Ralf Möller, Marcel Gehrke:
Lifted Causal Inference in Relational Domains. 827-842 - Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge:
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions. 843-867 - Romain Lopez, Jan-Christian Hütter, Ehsan Hajiramezanali, Jonathan K. Pritchard, Aviv Regev:
Toward the Identifiability of Comparative Deep Generative Models. 868-912 - Yanai Elazar, Jiayao Zhang, David Wadden, Bo Zhang, Noah A. Smith:
Estimating the Causal Effect of Early ArXiving on Paper Acceptance. 913-933 - Tobias Hatt, Stefan Feuerriegel:
Sequential Deconfounding for Causal Inference with Unobserved Confounders. 934-956 - Michaela Hardt, William Roy Orchard, Patrick Blöbaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan:
The PetShop Dataset - Finding Causes of Performance Issues across Microservices. 957-978 - Kevin Debeire, Andreas Gerhardus, Jakob Runge, Veronika Eyring:
Bootstrap aggregation and confidence measures to improve time series causal discovery. 979-1007 - Kang Du, Yu Xiang:
Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning. 1008-1032 - Thomas Cook, Alan Mishler, Aaditya Ramdas:
Semiparametric Efficient Inference in Adaptive Experiments. 1033-1064 - Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaassen, Martin Spindler:
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study. 1065-1117 - Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le:
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. 1118-1140 - Alvaro Ribot, Chandler Squires, Caroline Uhler:
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models. 1141-1175 - Itai Feigenbaum, Devansh Arpit, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese, Huan Wang:
Causal Layering via Conditional Entropy. 1176-1191 - Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing:
Meaningful Causal Aggregation and Paradoxical Confounding. 1192-1217 - Søren Wengel Mogensen, Karin Rathsman, Per Nilsson:
Causal discovery in a complex industrial system: A time series benchmark. 1218-1236 - Wenqin Liu, Biwei Huang, Erdun Gao, Qiuhong Ke, Howard D. Bondell, Mingming Gong:
Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach. 1237-1263
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2026-05-05 23:49 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
