unobserved confounder

A Convex Framework for Confounding Robust Inference JMLR 2026 Detecting Unobserved Confounders: A Kernelized Regression Approach AAAI 2026 Spatio-Temporal Hierarchical Causal Models AAAI 2026 Kernel Single Proxy Control for Deterministic Confounding AISTATS 2025 Scalable Out-of-Distribution Robustness in the Presence of Unobserved Confounders AISTATS 2025 Spectral Representation for Causal Estimation with Hidden Confounders AISTATS 2025 Causal Imitation for Markov Decision Processes: a Partial Identification Approach NeurIPS 2024 Causally Aligned Curriculum Learning ICLR 2024 Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimand NeurIPS 2024 Linear-Time Algorithms for Front-Door Adjustment in Causal Graphs AAAI 2024 Offline Policy Evaluation and Optimization Under Confounding AISTATS 2024 Scores for Learning Discrete Causal Graphs with Unobserved Confounders AAAI 2024 Sequential Deconfounding for Causal Inference with Unobserved Confounders CLeaR 2024 Approximate Allocation Matching for Structural Causal Bandits with Unobserved Confounders NeurIPS 2023 Causal Bounds in Quasi-Markovian Graphs ICML 2023 Causal Imitation Learning via Inverse Reinforcement Learning ICLR 2023 Kernel Conditional Moment Constraints for Confounding Robust Inference AISTATS 2023 The Proximal ID Algorithm JMLR 2023 Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders NeurIPS 2022 Learning Human Driving Behaviors with Sequential Causal Imitation Learning AAAI 2022 Scalable Computation of Causal Bounds ICML 2022 A Proxy Variable View of Shared Confounding ICML 2021 Sequential Causal Imitation Learning with Unobserved Confounders NeurIPS 2021 Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding AAAI 2019 Counterfactual Data-Fusion for Online Reinforcement Learners ICML 2017