Saved in:
| Main Author: | Wang, Zhu |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.10204 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Multi-Label Residual Weighted Learning for Individualized Combination Treatment Rule
by: Xu, Qi, et al.
Published: (2023)
by: Xu, Qi, et al.
Published: (2023)
Fusing Individualized Treatment Rules Using Secondary Outcomes
by: Gao, Daiqi, et al.
Published: (2024)
by: Gao, Daiqi, et al.
Published: (2024)
Trajectory-Based Individualized Treatment Rules
by: Yao, Lanqiu, et al.
Published: (2024)
by: Yao, Lanqiu, et al.
Published: (2024)
Demographic Parity-aware Individualized Treatment Rules
by: Cui, Wenhai, et al.
Published: (2025)
by: Cui, Wenhai, et al.
Published: (2025)
Safe Individualized Treatment Rules with Controllable Harm Rates
by: Wu, Peng, et al.
Published: (2025)
by: Wu, Peng, et al.
Published: (2025)
Guidance on Individualized Treatment Rule Estimation in High Dimensions
by: Boileau, Philippe, et al.
Published: (2023)
by: Boileau, Philippe, et al.
Published: (2023)
Evaluating Surrogates in Individualized Treatment Rules
by: Xu, Zeyu, et al.
Published: (2025)
by: Xu, Zeyu, et al.
Published: (2025)
Adaptive Weight Learning for Multiple Outcome Optimization With Continuous Treatment
by: Wang, Chang, et al.
Published: (2024)
by: Wang, Chang, et al.
Published: (2024)
Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes
by: Lu, Yuying, et al.
Published: (2026)
by: Lu, Yuying, et al.
Published: (2026)
Scalable and Distributed Individualized Treatment Rules for Massive Datasets
by: Qiao, Nan, et al.
Published: (2025)
by: Qiao, Nan, et al.
Published: (2025)
Transfer Learning for Classification under Decision Rule Drift with Application to Optimal Individualized Treatment Rule Estimation
by: Wang, Xiaohan, et al.
Published: (2025)
by: Wang, Xiaohan, et al.
Published: (2025)
Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules under Effect Heterogeneity
by: Oh, Eun Jeong, et al.
Published: (2025)
by: Oh, Eun Jeong, et al.
Published: (2025)
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
by: Li, Michael Lingzhi, et al.
Published: (2024)
by: Li, Michael Lingzhi, et al.
Published: (2024)
Multicategory Matched Learning for Estimating Optimal Individualized Treatment Rules in Observational Studies with Application to a Hepatocellular Carcinoma Study
by: Li, Xuqiao, et al.
Published: (2023)
by: Li, Xuqiao, et al.
Published: (2023)
Bayesian Outcome Weighted Learning
by: Yazzourh, Sophia, et al.
Published: (2024)
by: Yazzourh, Sophia, et al.
Published: (2024)
Differentially Private Estimation of Weighted Average Treatment Effects for Binary Outcomes
by: Guha, Sharmistha, et al.
Published: (2024)
by: Guha, Sharmistha, et al.
Published: (2024)
Locally Interpretable Individualized Treatment Rules for Black-Box Decision Models
by: Charvadeh, Yasin Khadem, et al.
Published: (2026)
by: Charvadeh, Yasin Khadem, et al.
Published: (2026)
Doubly-Robust Bayesian Estimation of Optimal Individualized Treatment Rules using Network Meta-Analysis
by: Wigle, Augustine, et al.
Published: (2026)
by: Wigle, Augustine, et al.
Published: (2026)
Adaptive Targeted Maximum Likelihood Estimation of the Mean Potential Outcome under a Treatment Rule
by: Xu, Yichen, et al.
Published: (2026)
by: Xu, Yichen, et al.
Published: (2026)
Quantifying Individual Risk for Binary Outcomes
by: Wu, Peng, et al.
Published: (2024)
by: Wu, Peng, et al.
Published: (2024)
Quantile Outcome Adaptive Lasso: Covariate Selection for Inverse Probability Weighting Estimator of Quantile Treatment Effects
by: Shoji, Takehiro, et al.
Published: (2024)
by: Shoji, Takehiro, et al.
Published: (2024)
CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
by: Zhou, Jiehui, et al.
Published: (2024)
by: Zhou, Jiehui, et al.
Published: (2024)
Bayesian Machine Learning for Estimating Optimal Dynamic Treatment Regimes with Ordinal Outcomes
by: Wang, Xinru, et al.
Published: (2025)
by: Wang, Xinru, et al.
Published: (2025)
Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data
by: Yu, Shuguang, et al.
Published: (2026)
by: Yu, Shuguang, et al.
Published: (2026)
A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting
by: Wu, Zeqi, et al.
Published: (2025)
by: Wu, Zeqi, et al.
Published: (2025)
Nonparametric Bounds for Evaluating the Clinical Utility of Treatment Rules
by: Hruza, Johannes, et al.
Published: (2025)
by: Hruza, Johannes, et al.
Published: (2025)
Learning Robust Treatment Rules for Censored Data
by: Cui, Yifan, et al.
Published: (2024)
by: Cui, Yifan, et al.
Published: (2024)
Qini Curves for Multi-Armed Treatment Rules
by: Sverdrup, Erik, et al.
Published: (2023)
by: Sverdrup, Erik, et al.
Published: (2023)
PAC-Bayesian Reward-Certified Outcome Weighted Learning
by: Ishikawa, Yuya, et al.
Published: (2026)
by: Ishikawa, Yuya, et al.
Published: (2026)
Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity
by: Sechidis, Konstantinos, et al.
Published: (2025)
by: Sechidis, Konstantinos, et al.
Published: (2025)
Sharp Bounds for Treatment Effect Generalization under Outcome Distribution Shift
by: Asiaee, Amir, et al.
Published: (2026)
by: Asiaee, Amir, et al.
Published: (2026)
A New Causal Rule Learning Approach to Interpretable Estimation of Heterogeneous Treatment Effect
by: Wu, Ying, et al.
Published: (2023)
by: Wu, Ying, et al.
Published: (2023)
Outcome-Informed Weighting for Robust ATE Estimation
by: Yang, Linying, et al.
Published: (2025)
by: Yang, Linying, et al.
Published: (2025)
A Novel Stratified Analysis Method for Testing and Estimating Overall Treatment Effects on Time-to-Event Outcomes Using Average Hazard with Survival Weight
by: Qian, Zihan, et al.
Published: (2024)
by: Qian, Zihan, et al.
Published: (2024)
On Weighted Orthogonal Learners for Heterogeneous Treatment Effects
by: Morzywolek, Pawel, et al.
Published: (2023)
by: Morzywolek, Pawel, et al.
Published: (2023)
On the Graphical Rules for Recovering the Average Treatment Effect Under Selection Bias
by: Zhang, Yichi, et al.
Published: (2025)
by: Zhang, Yichi, et al.
Published: (2025)
Individualized Causal Effects under Network Interference with Combinatorial Treatments
by: Lu, Yunping, et al.
Published: (2026)
by: Lu, Yunping, et al.
Published: (2026)
Impossibility of Distribution-Free Predictive Inference for Individual Treatment Effects
by: Tao, Chongguang, et al.
Published: (2026)
by: Tao, Chongguang, et al.
Published: (2026)
Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory
by: Gilbert, Joshua B., et al.
Published: (2024)
by: Gilbert, Joshua B., et al.
Published: (2024)
Inference on Individual Treatment Effects in Nonseparable Triangular Models
by: Ma, Jun, et al.
Published: (2021)
by: Ma, Jun, et al.
Published: (2021)
Similar Items
-
Multi-Label Residual Weighted Learning for Individualized Combination Treatment Rule
by: Xu, Qi, et al.
Published: (2023) -
Fusing Individualized Treatment Rules Using Secondary Outcomes
by: Gao, Daiqi, et al.
Published: (2024) -
Trajectory-Based Individualized Treatment Rules
by: Yao, Lanqiu, et al.
Published: (2024) -
Demographic Parity-aware Individualized Treatment Rules
by: Cui, Wenhai, et al.
Published: (2025) -
Safe Individualized Treatment Rules with Controllable Harm Rates
by: Wu, Peng, et al.
Published: (2025)