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20142022
most citedEfficient augmentation and relaxation learning for individualized treatment rules using observational data

78 citations · 206 across the 26 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

stat.ME2019★ 9 cited

Heterogeneity-aware and communication-efficient distributed statistical inference

Rui Duan, Yang Ning, Yong Chen

In multicenter research, individual-level data are often protected against sharing across sites. To overcome the barrier of data sharing, many distributed algorithms, which only re…

stat.ME2019★ 1 cited

Regression Discontinuity Design under Self-selection

Sida Peng, Yang Ning

In Regression Discontinuity (RD) design, self-selection leads to different distributions of covariates on two sides of the policy intervention, which essentially violates the conti…

stat.ME2019

Optimal Sampling for Generalized Linear Models under Measurement Constraints

Tao Zhang, Yang Ning, David Ruppert

Under "measurement constraints," responses are expensive to measure and initially unavailable on most of records in the dataset, but the covariates are available for the entire dat…

math.ST2019★ 4 cited

Nonregular and Minimax Estimation of Individualized Thresholds in High Dimension with Binary Responses

Huijie Feng, Yang Ning, Jiwei Zhao

Given a large number of covariates , we consider the estimation of a high-dimensional parameter in an individualized linear threshold for a continuous variable ,…

stat.ME2019★ 78 cited

Efficient augmentation and relaxation learning for individualized treatment rules using observational data

Ying-Qi Zhao, Eric B. Laber, Yang Ning +2

Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied. A globally aging population, rising healthcare costs, and increased access…