Caroline Uhler

Meta-Dependence in Conditional Independence Testing UAI 2026 Relaxing Faithfulness with Intervention-Only Causal Discovery UAI 2026 An Information Criterion for Controlled Disentanglement of Multimodal Data ICLR 2025 Probabilistic Factorial Experimental Design for Combinatorial Interventions ICML 2025 Synthetic Potential Outcomes and Causal Mixture Identifiability AISTATS 2025 Causal Discovery under Off-Target Interventions AISTATS 2024 Causal Discovery with Fewer Conditional Independence Tests ICML 2024 Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models CLeaR 2024 Identifiability Guarantees for Causal Disentanglement from Purely Observational Data NeurIPS 2024 Learning Mixtures of Unknown Causal Interventions NeurIPS 2024 Membership Testing in Markov Equivalence Classes via Independence Queries AISTATS 2024 Removing Biases from Molecular Representations via Information Maximization ICLR 2024 Identifiability Guarantees for Causal Disentanglement from Soft Interventions NeurIPS 2023 Linear Causal Disentanglement via Interventions ICML 2023 Meek Separators and Their Applications in Targeted Causal Discovery NeurIPS 2023 Unpaired Multi-Domain Causal Representation Learning NeurIPS 2023 Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation NeurIPS 2023 Causal Imputation via Synthetic Interventions CLeaR 2022 Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors CLeaR 2022 Joint Inference of Multiple Graphs from Matrix Polynomials JMLR 2022 Matching a Desired Causal State via Shift Interventions NeurIPS 2021 Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis CVPR 2021 Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning NeurIPS 2021 Anchored Causal Inference in the Presence of Measurement Error UAI 2020 Causal Structure Discovery from Distributions Arising from Mixtures of DAGs ICML 2020 Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters AISTATS 2020 Ordering-Based Causal Structure Learning in the Presence of Latent Variables AISTATS 2020 Permutation-Based Causal Structure Learning with Unknown Intervention Targets UAI 2020 ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery AISTATS 2019 Scalable Unbalanced Optimal Transport using Generative Adversarial Networks ICLR 2019 Size of Interventional Markov Equivalence Classes in random DAG models AISTATS 2019 Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions ICML 2018 Direct Estimation of Differences in Causal Graphs NeurIPS 2018 Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models ICML 2018 Permutation-based Causal Inference Algorithms with Interventions NeurIPS 2017