CLeaR 2024

45 papers

Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach

Wenqin Liu, Biwei Huang, Erdun Gao, Qiuhong Ke, Howard Bondell

Causal discovery in a complex industrial system: A time series benchmark

Søren Wengel Mogensen, Karin Rathsman, Per Nilsson

Meaningful Causal Aggregation and Paradoxical Confounding

Yuchen Zhu, Kailash Budhathoki, Jonas M. Kübler, Dominik Janzing

Causal Layering via Conditional Entropy

Itai Feigenbaum, Devansh Arpit, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao

Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models

Álvaro Ribot, Chandler Squires, Caroline Uhler

Scalable Counterfactual Distribution Estimation in Multivariate Causal Models

Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le

Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study

Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaassen, Martin Spindler

Semiparametric Efficient Inference in Adaptive Experiments

Thomas Cook, Alan Mishler, Aaditya Ramdas

Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning

Kang Du, Yu Xiang

Bootstrap aggregation and confidence measures to improve time series causal discovery

Kevin Debeire, Andreas Gerhardus, Jakob Runge, Veronika Eyring

The PetShop Dataset — Finding Causes of Performance Issues across Microservices

Michaela Hardt, William Roy Orchard, Patrick Blöbaum, Elke Kirschbaum, Shiva Kasiviswanathan

Sequential Deconfounding for Causal Inference with Unobserved Confounders

Tobias Hatt, Stefan Feuerriegel

Estimating the Causal Effect of Early ArXiving on Paper Acceptance

Yanai Elazar, Jiayao Zhang, David Wadden, Bo Zhang, Noah A. Smith

Toward the Identifiability of Comparative Deep Generative Models

Romain Lopez, Jan-Christian Huetter, Ehsan Hajiramezanali, Jonathan K Pritchard, Aviv Regev

Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions

Simon Bing, Urmi Ninad, Jonas Wahl, Jakob Runge

Lifted Causal Inference in Relational Domains

Malte Luttermann, Mattis Hartwig, Tanya Braun, Ralf Möller, Marcel Gehrke

Inference of nonlinear causal effects with application to TWAS with GWAS summary data

Ben Dai, Chunlin Li, Haoran Xue, Wei Pan, Xiaotong Shen

A causality-inspired plus-minus model for player evaluation in team sports

Caterina De Bacco, Yixin Wang, David Blei

DiConStruct: Causal Concept-based Explanations through Black-Box Distillation

Ricardo Miguel de Oliveira Moreira, Jacopo Bono, Mário Cardoso, Pedro Saleiro, Mario A. T. Figueiredo

Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice

Damian Machlanski, Spyridon Samothrakis, Paul S Clarke

Causal Matching using Random Hyperplane Tessellations

Abhishek Dalvi, Neil Ashtekar, Vasant G Honavar

Causality for Functional Longitudinal Data

Andrew Ying

Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment

Jan Corazza, Hadi Partovi Aria, Daniel Neider, Zhe Xu

causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery

Konstantin Göbler, Tobias Windisch, Mathias Drton, Tim Pychynski, Martin Roth

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens

Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner

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

Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift

Philip Boeken, Onno Zoeter, Joris Mooij

On the Lasso for Graphical Continuous Lyapunov Models

Philipp Dettling, Mathias Drton, Mladen Kolar

Implicit and Explicit Policy Constraints for Offline Reinforcement Learning

Yang Liu, Marius Hofert

Causal Optimal Transport of Abstractions

Yorgos Felekis, Fabio Massimo Zennaro, Nicola Branchini, Theodoros Damoulas

Confounded Budgeted Causal Bandits

Fateme Jamshidi, Jalal Etesami, Negar Kiyavash

On the Identifiability of Quantized Factors

Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent

Causal Discovery Under Local Privacy

Ruta Binkyte, Carlos Antonio Pinzón, Szilvia Lestyán, Kangsoo Jung, Héber Hwang Arcolezi

Towards the Reusability and Compositionality of Causal Representations

Davide Talon, Phillip Lippe, Stuart James, Alessio Del Bue, Sara Magliacane

Extracting the Multiscale Causal Backbone of Brain Dynamics

Gabriele D\textsc\char13Acunto, Francesco Bonchi, Gianmarco De Francisci Morales, Giovanni Petri

Pragmatic Fairness: Developing Policies with Outcome Disparity Control

Limor Gultchin, Siyuan Guo, Alan Malek, Silvia Chiappa, Ricardo Silva

Bicycle: Intervention-Based Causal Discovery with Cycles

Martin Rohbeck, Brian Clarke, Katharina Mikulik, Alexandra Pettet, Oliver Stegle

Fundamental Properties of Causal Entropy and Information Gain

Francisco N. F. Q. Simoes, Mehdi Dastani, Thijs van Ommen

Finding Alignments Between Interpretable Causal Variables and Distributed Neural Representations

Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, Noah Goodman

Cautionary Tales on Synthetic Controls in Survival Analyses

Alicia Curth, Hoifung Poon, Aditya V. Nori, Javier Gonzalez

Causal State Distillation for Explainable Reinforcement Learning

Wenhao Lu, Xufeng Zhao, Thilo Fryen, Jae Hee Lee, Mengdi Li

Structure Learning with Continuous Optimization: A Sober Look and Beyond

Ignavier Ng, Biwei Huang, Kun Zhang

An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis

Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar

Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding

Myrl G. Marmarelis, Greg Ver Steeg, Aram Galstyan, Fred Morstatter

Dual Likelihood for Causal Inference under Structure Uncertainty

David Strieder, Mathias Drton