inverse probability weighting

Doubly Debiased Robust Subsampling for Transfer Learning JMLR 2026 Wasserstein Policy Learning for Distributional Outcomes COLT 2026 Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection JMLR 2025 Distributionally Robust Policy Evaluation and Learning for Continuous Treatment with Observational Data AAAI 2025 Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings JMLR 2025 Incremental Causal Effect for Time to Treatment Initialization ICLR 2025 Off-Policy Evaluation under Nonignorable Missing Data ICML 2025 Stabilized Inverse Probability Weighting via Isotonic Calibration CLeaR 2025 Temporal Inverse Probability Weighting for Causal Discovery in Controlled Before–After Studies: Discovering ADEs in Generics CLeaR 2025 An effective framework for estimating individualized treatment rules NeurIPS 2024 Causality for Functional Longitudinal Data CLeaR 2024 Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation UAI 2024 Scale-invariant Optimal Sampling for Rare-events Data and Sparse Models NeurIPS 2024 Stage-Aware Learning for Dynamic Treatments JMLR 2024 Causal inference with outcome-dependent missingness and self-censoring UAI 2023 High Precision Causal Model Evaluation with Conditional Randomization NeurIPS 2023 PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference NeurIPS 2023 The Brier Score under Administrative Censoring: Problems and a Solution JMLR 2023 Estimation of Local Average Treatment Effect by Data Combination AAAI 2022 Maximum sampled conditional likelihood for informative subsampling JMLR 2022 Respecting Transfer Gap in Knowledge Distillation NeurIPS 2022 Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data NeurIPS 2021 Estimating Causal Effects Using Weighting-Based Estimators AAAI 2020 Imitation-Regularized Offline Learning AISTATS 2019 Policy Evaluation and Optimization with Continuous Treatments AISTATS 2018