CLeaR 2023

39 papers

A Meta-Reinforcement Learning Algorithm for Causal Discovery Andreas W.M. Sauter, Erman Acar, Vincent Francois-Lavet

An Algorithm and Complexity Results for Causal Unit Selection Haiying Huang, Adnan Darwiche

Backtracking Counterfactuals Julius Von Kügelgen, Abdirisak Mohamed, Sander Beckers

Beyond the Markov Equivalence Class: Extending Causal Discovery Under Latent Confounding Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen

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Branch-Price-and-Cut for Causal Discovery James Cussens

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Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal

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Causal Abstraction with Soft Interventions Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu

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Causal Discovery for Non-Stationary Non-Linear Time Series Data Using Just-in-Time Modeling Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu

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Causal Discovery with Score Matching on Additive Models with Arbitrary Noise Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

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Causal Inference Despite Limited Global Confounding via Mixture Models Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard Schulman

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Causal Inference Under Interference and Model Uncertainty Chi Zhang, Karthika Mohan, Judea Pearl

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Causal Learning Through Deliberate Undersampling Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey Plis

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Causal Models with Constraints Sander Beckers, Joseph Halpern, Christopher Hitchcock

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Causal Triplet: An Open Challenge for Intervention-Centric Causal Representation Learning Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello

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Directed Graphical Models and Causal Discovery for Zero-Inflated Data Shiqing Yu, Mathias Drton, Ali Shojaie

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Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model Mario A. T. Figueiredo, Catarina Oliveira

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Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto

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Estimating Long-Term Causal Effects from Short-Term Experiments and Long-Term Observational Data with Unobserved Confounding Graham Van Goffrier, Lucas Maystre, Ciarán Mark Gilligan-Lee

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Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour Rhys Peter Matthew Howard, Lars Kunze

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Factorization of the Partial Covariance in Singly-Connected Path Diagrams Jose Peña

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Factual Observation Based Heterogeneity Learning for Counterfactual Prediction Hao Zou, Haotian Wang, Renzhe Xu, Bo Li, Jian Pei, Ye Jun Jian, Peng Cui

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Generalizing Clinical Trials with Convex Hulls Eric Strobl, Thomas A Lasko

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Image-Based Treatment Effect Heterogeneity Connor Thomas Jerzak, Fredrik Daniel Johansson, Adel Daoud

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Influence-Aware Attention for Multivariate Temporal Point Processes Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin Bennett

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Instrumental Processes Using Integrated Covariances Søren Wengel Mogensen

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Jointly Learning Consistent Causal Abstractions over Multiple Interventional Distributions Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei, W. Dhammika Widanage, Theodoros Damoulas

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Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan Pritchard, Aviv Regev

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Learning Conditional Granger Causal Temporal Networks Ananth Balashankar, Srikanth Jagabathula, Lakshmi Subramanian

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Leveraging Causal Graphs for Blocking in Randomized Experiments Abhishek Kumar Umrawal

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Local Causal Discovery for Estimating Causal Effects Shantanu Gupta, David Childers, Zachary Chase Lipton

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Local Dependence Graphs for Discrete Time Processes Wojciech Niemiro, Łukasz Rajkowski

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Non-Parametric Identifiability and Sensitivity Analysis of Synthetic Control Models Jakob Zeitler, Athanasios Vlontzos, Ciarán Mark Gilligan-Lee

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On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee

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On the Interventional Kullback-Leibler Divergence Jonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf

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Practical Algorithms for Orientations of Partially Directed Graphical Models Malte Luttermann, Marcel Wienöbst, Maciej Liskiewicz

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Sample-Specific Root Causal Inference with Latent Variables Eric Strobl, Thomas A Lasko

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Scalable Causal Discovery with Score Matching Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello

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Stochastic Causal Programming for Bounding Treatment Effects Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, Niki Kilbertus

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Unsupervised Object Learning via Common Fate Matthias Tangemann, Steffen Schneider, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kuemmerer, Matthias Bethge, Bernhard Schölkopf

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