hidden confounding

A Causal Target for Learning to Defer Under Hidden Confounding AAAI 2026 Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs UAI 2026 Boosted Control Functions: Distribution Generalization and Invariance in Confounded Models JMLR 2026 Density Ratio-based Proxy Causal Learning Without Density Ratios AISTATS 2025 Off-policy Predictive Control with Causal Sensitivity Analysis UAI 2025 Addressing Hidden Confounding with Heterogeneous Observational Datasets for Recommendation NeurIPS 2024 Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confounding ICLR 2024 Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding CLeaR 2024 Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis ICML 2024 When to Act and When to Ask: Policy Learning With Deferral Under Hidden Confounding NeurIPS 2024 B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding ICML 2023 Detecting hidden confounding in observational data using multiple environments NeurIPS 2023 High-Dimensional Inference for Generalized Linear Models with Hidden Confounding JMLR 2023 Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach NeurIPS 2023 Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data CLeaR 2022 Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions NeurIPS 2022 Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding ICML 2021 Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders UAI 2020 Identification and Estimation of Causal Effects from Dependent Data NeurIPS 2018 Removing Hidden Confounding by Experimental Grounding NeurIPS 2018