conditional average treatment effect

Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects ICML 2023 Conformal Meta-learners for Predictive Inference of Individual Treatment Effects NeurIPS 2023 Estimating Conditional Average Treatment Effects with Missing Treatment Information AISTATS 2023 Estimating individual treatment effects under unobserved confounding using binary instruments ICLR 2023 Image-based Treatment Effect Heterogeneity CLeaR 2023 Proximal Causal Learning of Conditional Average Treatment Effects ICML 2023 Robust and Agnostic Learning of Conditional Distributional Treatment Effects AISTATS 2023 Uncovering the Varied Impact of Behavioral Change Messages on Population Groups MLHC 2023 A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources ICML 2022 Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding NeurIPS 2022 Differentially Private Estimation of Heterogeneous Causal Effects CLeaR 2022 Feature selection for discovering distributional treatment effect modifiers UAI 2022 Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI MIDL 2022 Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation NeurIPS 2022 FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference JMLR 2021 Valid Causal Inference with (Some) Invalid Instruments ICML 2021 Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding AISTATS 2019