4 papers
Dependable Exploitation of High-Dimensional Unlabeled Data in an Assumption-Lean Framework
Chao Ying, Siyi Deng, Yang Ning +2
Semi-supervised learning has attracted significant attention due to the proliferation of applications featuring limited labeled data but abundant unlabeled data. In this paper, we…
Incorporating External Controls for Estimating the Average Treatment Effect on the Treated with High-Dimensional Data: Retaining Double Robustness and Ensuring Double Safety
Chi-Shian Dai, Chao Ying, Yang Ning +1
Randomized controlled trials (RCTs) are widely regarded as the gold standard for causal inference in biomedical research. For instance, when estimating the average treatment effect…
Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation
Chao Ying, Jun Jin, Haotian Zhang +4
We study an unsupervised domain adaptation problem where the source domain consists of subpopulations defined by the binary label and a binary background (or environment) .…
Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift
Chao Ying, Jun Jin, Yi Guo +3
Collecting gold-standard phenotype data via manual extraction is typically labor-intensive and slow, whereas automated computational phenotypes (ACPs) offer a systematic and much f…