CLeaR

Publications

2024

Ben Dai, Chunlin Li, Haoran Xue, Wei Pan, Xiaotong Shen
Inference of nonlinear causal effects with application to TWAS with GWAS summary data. CLeaR (2024)
Philipp Dettling, Mathias Drton, Mladen Kolar
On the Lasso for Graphical Continuous Lyapunov Models. CLeaR (2024)
Philip A. Boeken, Onno Zoeter, Joris M. Mooij
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift. CLeaR (2024)
Jan Corazza, Hadi Partovi Aria, Daniel Neider, Zhe Xu
Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment. CLeaR (2024)
Andrew Ying
Causality for Functional Longitudinal Data. CLeaR (2024)
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. CLeaR (2024)
Ruta Binkyte, Carlos Antonio Pinzóon, Szilvia Lestyan, Kangsoo Jung, Héber Hwang Arcolezi, Catuscia Palamidessi
Causal Discovery Under Local Privacy. CLeaR (2024)
Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models. CLeaR (2024)
Thomas Cook, Alan Mishler, Aaditya Ramdas
Semiparametric Efficient Inference in Adaptive Experiments. CLeaR (2024)
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. CLeaR (2024)
Fateme Jamshidi, Jalal Etesami, Negar Kiyavash
Confounded Budgeted Causal Bandits. CLeaR (2024)
Yang Liu, Marius Hofert
Implicit and Explicit Policy Constraints for Offline Reinforcement Learning. CLeaR (2024)
Vitória Barin Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent
On the Identifiability of Quantized Factors. CLeaR (2024)
Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini, Theodoros Damoulas
Causal Optimal Transport of Abstractions. CLeaR (2024)
Malte Luttermann, Mattis Hartwig, Tanya Braun, Ralf Möller, Marcel Gehrke
Lifted Causal Inference in Relational Domains. CLeaR (2024)
Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth, Steffen Sonntag
extttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery. CLeaR (2024)
Caterina De Bacco, Yixin Wang, David M. Blei
A causality-inspired plus-minus model for player evaluation in team sports. CLeaR (2024)
Ignavier Ng, Biwei Huang, Kun Zhang
Structure Learning with Continuous Optimization: A Sober Look and Beyond. CLeaR (2024)
Alicia Curth, Hoifung Poon, Aditya V. Nori, Javier González
Cautionary Tales on Synthetic Controls in Survival Analyses. CLeaR (2024)
David Strieder, Mathias Drton
Dual Likelihood for Causal Inference under Structure Uncertainty. CLeaR (2024)
Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah D. Goodman
Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations. CLeaR (2024)
Wenhao Lu, Xufeng Zhao, Thilo Fryen, Jae Hee Lee, Mengdi Li, Sven Magg, Stefan Wermter
Causal State Distillation for Explainable Reinforcement Learning. CLeaR (2024)
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. CLeaR (2024)
Yanai Elazar, Jiayao Zhang, David Wadden, Bo Zhang, Noah A. Smith
Estimating the Causal Effect of Early ArXiving on Paper Acceptance. CLeaR (2024)
Francisco Nunes Ferreira Quialheiro Simoes, Mehdi Dastani, Thijs van Ommen
Fundamental Properties of Causal Entropy and Information Gain. CLeaR (2024)
Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva
Pragmatic Fairness: Developing Policies with Outcome Disparity Control. CLeaR (2024)
Abhishek Dalvi, Neil Ashtekar, Vasant G. Honavar
Causal Matching using Random Hyperplane Tessellations. CLeaR (2024)
Michaela Hardt, William Roy Orchard, Patrick Blöbaum, Elke Kirschbaum, Shiva Prasad Kasiviswanathan
The PetShop Dataset - Finding Causes of Performance Issues across Microservices. CLeaR (2024)
Gabriele D'Acunto, Francesco Bonchi, Gianmarco De Francisci Morales, Giovanni Petri
Extracting the Multiscale Causal Backbone of Brain Dynamics. CLeaR (2024)
Romain Lopez, Jan-Christian Hütter, Ehsan Hajiramezanali, Jonathan K. Pritchard, Aviv Regev
Toward the Identifiability of Comparative Deep Generative Models. CLeaR (2024)
Davide Talon, Phillip Lippe, Stuart James, Alessio Del Bue, Sara Magliacane
Towards the Reusability and Compositionality of Causal Representations. CLeaR (2024)
Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan, Fred Morstatter
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding. CLeaR (2024)
Kang Du, Yu Xiang
Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning. CLeaR (2024)
Alvaro Ribot, Chandler Squires, Caroline Uhler
Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models. CLeaR (2024)
Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions. CLeaR (2024)
Damian Machlanski, Spyridon Samothrakis, Paul Clarke
Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice. CLeaR (2024)
Søren Wengel Mogensen, Karin Rathsman, Per Nilsson
Causal discovery in a complex industrial system: A time series benchmark. CLeaR (2024)
Itai Feigenbaum, Devansh Arpit, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese, Huan Wang
Causal Layering via Conditional Entropy. CLeaR (2024)
Kevin Debeire, Andreas Gerhardus, Jakob Runge, Veronika Eyring
Bootstrap aggregation and confidence measures to improve time series causal discovery. CLeaR (2024)
Tobias Hatt, Stefan Feuerriegel
Sequential Deconfounding for Causal Inference with Unobserved Confounders. CLeaR (2024)
Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis. CLeaR (2024)
Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaassen, Martin Spindler
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study. CLeaR (2024)
Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing
Meaningful Causal Aggregation and Paradoxical Confounding. CLeaR (2024)
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. CLeaR (2024)
Martin Rohbeck, Brian Clarke, Katharina Mikulik, Alexandra Pettet, Oliver Stegle, Kai Ueltzhöffer
Bicycle: Intervention-Based Causal Discovery with Cycles. CLeaR (2024)

volume 236, 2024

Causal Learning and Reasoning, 1-3 April 2024, Los Angeles, California, USA. CLeaR 236 (2024)

2023

volume 213, 2023

Conference on Causal Learning and Reasoning, CLeaR 2023, 11-14 April 2023, Amazon Development Center, Tübingen, Germany, April 11-14, 2023. CLeaR 213 (2023)