CLeaR

Keyphrases

Publications

2023

Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus
Stochastic Causal Programming for Bounding Treatment Effects. CLeaR (2023)
Eric Strobl, Thomas A Lasko
Generalizing Clinical Trials with Convex Hulls. CLeaR (2023)
Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu
Causal Abstraction with Soft Interventions. CLeaR (2023)
Haiying Huang, Adnan Darwiche
An Algorithm and Complexity Results for Causal Unit Selection. CLeaR (2023)
Ananth Balashankar, Srikanth Jagabathula, Lakshmi Subramanian
Learning Conditional Granger Causal Temporal Networks. CLeaR (2023)
Abhishek Kumar Umrawal
Leveraging Causal Graphs for Blocking in Randomized Experiments. CLeaR (2023)
Eric V. Strobl, Thomas A. Lasko
Sample-Specific Root Causal Inference with Latent Variables. CLeaR (2023)
Sander Beckers, Joseph Y. Halpern, Christopher Hitchcock
Causal Models with Constraints. CLeaR (2023)
Chi Zhang, Karthika Mohan, Judea Pearl
Causal Inference under Interference and Model Uncertainty. CLeaR (2023)
Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang, Sanghack Lee
On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition. CLeaR (2023)
Graham Van Goffrier, Lucas Maystre, Ciarán Mark Gilligan-Lee
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding. CLeaR (2023)
Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CLeaR (2023)
Søren Wengel Mogensen
Instrumental Processes Using Integrated Covariances. CLeaR (2023)
Fabio Massimo Zennaro, Máté Drávucz, Geanina Apachitei, Widanalage Dhammika Widanage, Theodoros Damoulas
Jointly Learning Consistent Causal Abstractions Over Multiple Interventional Distributions. CLeaR (2023)
Xiao Shou, Tian Gao, Dharmashankar Subramanian, Debarun Bhattacharjya, Kristin P. Bennett
Influence-Aware Attention for Multivariate Temporal Point Processes. CLeaR (2023)
Jose M. Peña
Factorization of the Partial Covariance in Singly-Connected Path Diagrams. CLeaR (2023)
Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. CLeaR (2023)
Shantanu Gupta, David Childers, Zachary Chase Lipton
Local Causal Discovery for Estimating Causal Effects. CLeaR (2023)
Jakob Zeitler, Athanasios Vlontzos, Ciarán Mark Gilligan-Lee
Non-parametric identifiability and sensitivity analysis of synthetic control models. CLeaR (2023)
Matthias Tangemann, Steffen Schneider, Julius von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kümmerer, Matthias Bethge, Bernhard Schölkopf
Unsupervised Object Learning via Common Fate. CLeaR (2023)
Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey M. Plis
Causal Learning through Deliberate Undersampling. CLeaR (2023)
Yuejiang Liu, Alexandre Alahi, Chris Russell, Max Horn, Dominik Zietlow, Bernhard Schölkopf, Francesco Locatello
Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. CLeaR (2023)
Julius von Kügelgen, Abdirisak Mohamed, Sander Beckers
Backtracking Counterfactuals. CLeaR (2023)
Rhys Howard, Lars Kunze
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour. CLeaR (2023)
Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman
Causal Inference Despite Limited Global Confounding via Mixture Models. CLeaR (2023)
Daigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu
Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling. CLeaR (2023)
James Cussens
Branch-Price-and-Cut for Causal Discovery. CLeaR (2023)
Wojciech Niemiro, Lukasz Rajkowski
Local Dependence Graphs for Discrete Time Processes. CLeaR (2023)
Mário A. T. Figueiredo, Catarina A. Oliveira
Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model. CLeaR (2023)
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello
Scalable Causal Discovery with Score Matching. CLeaR (2023)
Jonas Bernhard Wildberger, Siyuan Guo, Arnab Bhattacharyya, Bernhard Schölkopf
On the Interventional Kullback-Leibler Divergence. CLeaR (2023)
Malte Luttermann, Marcel Wienöbst, Maciej Liskiewicz
Practical Algorithms for Orientations of Partially Directed Graphical Models. CLeaR (2023)
Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. CLeaR (2023)
Andreas W. M. Sauter, Erman Acar, Vincent François-Lavet
A Meta-Reinforcement Learning Algorithm for Causal Discovery. CLeaR (2023)
Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto
Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios. CLeaR (2023)
Shiqing Yu, Mathias Drton, Ali Shojaie
Directed Graphical Models and Causal Discovery for Zero-Inflated Data. CLeaR (2023)
Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Kun Zhang, Francesco Locatello
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. CLeaR (2023)
Hao Zou, Haotian Wang, Renzhe Xu, Bo Li, Jian Pei, Ye Jun Jian, Peng Cui
Factual Observation Based Heterogeneity Learning for Counterfactual Prediction. CLeaR (2023)
Connor Thomas Jerzak, Fredrik Daniel Johansson, Adel Daoud
Image-based Treatment Effect Heterogeneity. CLeaR (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)

2022

volume 177, 2022

1st Conference on Causal Learning and Reasoning, CLeaR 2022, Sequoia Conference Center, Eureka, CA, USA, 11-13 April, 2022 CLeaR 177 (2022)