CLeaR 2024

45 papers

$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth, Steffen Sonntag

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A Causality-Inspired Plus-Minus Model for Player Evaluation in Team Sports Caterina De Bacco, Yixin Wang, David Blei

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An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar

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Bicycle: Intervention-Based Causal Discovery with Cycles Martin Rohbeck, Brian Clarke, Katharina Mikulik, Alexandra Pettet, Oliver Stegle, Kai Ueltzhöffer

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Bootstrap Aggregation and Confidence Measures to Improve Time Series Causal Discovery Kevin Debeire, Andreas Gerhardus, Jakob Runge, Veronika Eyring

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Causal Discovery in a Complex Industrial System: A Time Series Benchmark Søren Wengel Mogensen, Karin Rathsman, Per Nilsson

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Causal Discovery Under Local Privacy Ruta Binkyte, Carlos Antonio Pinzón, Szilvia Lestyán, Kangsoo Jung, Héber Hwang Arcolezi, Catuscia Palamidessi

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Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach Wenqin Liu, Biwei Huang, Erdun Gao, Qiuhong Ke, Howard Bondell, Mingming Gong

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Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models Álvaro Ribot, Chandler Squires, Caroline Uhler

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Causal Layering via Conditional Entropy Itai Feigenbaum, Devansh Arpit, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese, Huan Wang

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Causal Matching Using Random Hyperplane Tessellations Abhishek Dalvi, Neil Ashtekar, Vasant G Honavar

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Causal Optimal Transport of Abstractions Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini, Theodoros Damoulas

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Causal State Distillation for Explainable Reinforcement Learning Wenhao Lu, Xufeng Zhao, Thilo Fryen, Jae Hee Lee, Mengdi Li, Sven Magg, Stefan Wermter

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Causality for Functional Longitudinal Data Andrew Ying

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Cautionary Tales on Synthetic Controls in Survival Analyses Alicia Curth, Hoifung Poon, Aditya V. Nori, Javier González

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Confounded Budgeted Causal Bandits Fateme Jamshidi, Jalal Etesami, Negar Kiyavash

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Designing Monitoring Strategies for Deployed Machine Learning Algorithms: Navigating Performativity Through a Causal Lens Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Anthony Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia

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DiConStruct: Causal Concept-Based Explanations Through Black-Box Distillation Ricardo Miguel de Oliveira Moreira, Jacopo Bono, Mário Cardoso, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

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Dual Likelihood for Causal Inference Under Structure Uncertainty David Strieder, Mathias Drton

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Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding Myrl G. Marmarelis, Greg Steeg, Aram Galstyan, Fred Morstatter

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Estimating the Causal Effect of Early ArXiving on Paper Acceptance Yanai Elazar, Jiayao Zhang, David Wadden, Bo Zhang, Noah A. Smith

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Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift Philip Boeken, Onno Zoeter, Joris Mooij

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Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment Jan Corazza, Hadi Partovi Aria, Daniel Neider, Zhe Xu

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Extracting the Multiscale Causal Backbone of Brain Dynamics Gabriele D\textsc\char13Acunto, Francesco Bonchi, Gianmarco De Francisci Morales, Giovanni Petri

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Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah Goodman

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Fundamental Properties of Causal Entropy and Information Gain Francisco N. F. Q. Simoes, Mehdi Dastani, Thijs van Ommen

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Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaassen, Martin Spindler

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Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge

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Implicit and Explicit Policy Constraints for Offline Reinforcement Learning Yang Liu, Marius Hofert

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Inference of Nonlinear Causal Effects with Application to TWAS with GWAS Summary Data Ben Dai, Chunlin Li, Haoran Xue, Wei Pan, Xiaotong Shen

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Lifted Causal Inference in Relational Domains Malte Luttermann, Mattis Hartwig, Tanya Braun, Ralf Möller, Marcel Gehrke

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Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning Kang Du, Yu Xiang

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Meaningful Causal Aggregation and Paradoxical Confounding Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing

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On the Identifiability of Quantized Factors Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent

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On the Impact of Neighbourhood Sampling to Satisfy Sufficiency and Necessity Criteria in Explainable AI Urja Pawar, Christian Beder, Ruairi O\textsc\char13Reilly, Donna O\textsc\char13Shea

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On the Lasso for Graphical Continuous Lyapunov Models Philipp Dettling, Mathias Drton, Mladen Kolar

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Pragmatic Fairness: Developing Policies with Outcome Disparity Control Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva

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Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice Damian Machlanski, Spyridon Samothrakis, Paul S Clarke

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Scalable Counterfactual Distribution Estimation in Multivariate Causal Models Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le

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Semiparametric Efficient Inference in Adaptive Experiments Thomas Cook, Alan Mishler, Aaditya Ramdas

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Sequential Deconfounding for Causal Inference with Unobserved Confounders Tobias Hatt, Stefan Feuerriegel

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Structure Learning with Continuous Optimization: A Sober Look and Beyond Ignavier Ng, Biwei Huang, Kun Zhang

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The PetShop Dataset — Finding Causes of Performance Issues Across Microservices Michaela Hardt, William Roy Orchard, Patrick Blöbaum, Elke Kirschbaum, Shiva Kasiviswanathan

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Toward the Identifiability of Comparative Deep Generative Models Romain Lopez, Jan-Christian Huetter, Ehsan Hajiramezanali, Jonathan K Pritchard, Aviv Regev

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Towards the Reusability and Compositionality of Causal Representations Davide Talon, Phillip Lippe, Stuart James, Alessio Del Bue, Sara Magliacane

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