interventional data

Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions CLeaR 2026 Coarsening Causal DAG Models CLeaR 2026 The relative value of interventional and observational samples in Bayesian Causal Linear Gaussian Models UAI 2026 Information-Theoretic Causal Discovery in Topological Order AISTATS 2025 Learning Invariant Causal Mechanism from Vision-Language Models ICML 2025 $Ψ$net: Efficient Causal Modeling at Scale PGM 2024 Adaptive Online Experimental Design for Causal Discovery ICML 2024 Causal Optimal Transport of Abstractions CLeaR 2024 Sample Efficient Bayesian Learning of Causal Graphs from Interventions NeurIPS 2024 Targeted Reduction of Causal Models UAI 2024 Causal discovery from observational and interventional data across multiple environments NeurIPS 2023 Causal effect estimation from observational and interventional data through matrix weighted linear estimators UAI 2023 Causal Structure Learning for Latent Intervened Non-stationary Data ICML 2023 Interventional Causal Representation Learning ICML 2023 Learning to Induce Causal Structure ICLR 2023 Meek Separators and Their Applications in Targeted Causal Discovery NeurIPS 2023 NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning AISTATS 2023 Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery NeurIPS 2023 Active Bayesian Causal Inference NeurIPS 2022 Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets AAAI 2022 Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning NeurIPS 2022 IDYNO: Learning Nonparametric DAGs from Interventional Dynamic Data ICML 2022 Verification and search algorithms for causal DAGs NeurIPS 2022 Dynamic Causal Bayesian Optimization NeurIPS 2021 Selecting Data Augmentation for Simulating Interventions ICML 2021