b'Shu Yang'

research

∙ 07/21/2023

Multiple bias-calibration for adjusting selection bias of non-probability samples using data integration

Valid statistical inference is challenging when the sample is subject to...

0 Zhonglei Wang, et al. ∙

research

∙ 07/10/2023

Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator

Randomized experiments (REs) are the cornerstone for treatment effect ev...

0 Yuwen Cheng, et al. ∙

research

∙ 06/29/2023

Integrating Randomized Placebo-Controlled Trial Data with External Controls: A Semiparametric Approach with Selective Borrowing

In recent years, real-world external controls (ECs) have grown in popula...

0 Chenyin Gao, et al. ∙

research

∙ 05/28/2023

Pretest estimation in combining probability and non-probability samples

Multiple heterogeneous data sources are becoming increasingly available ...

0 Chenyin Gao, et al. ∙

research

∙ 05/23/2023

Augmented match weighted estimators for average treatment effects

Propensity score matching (PSM) and augmented inverse propensity weighti...

0 Tanchumin Xu, et al. ∙

research

∙ 04/18/2023

Quadruply robust estimation of marginal structural models in observational studies subject to covariate-driven observations

Electronic health records and other sources of observational data are in...

0 Janie Coulombe, et al. ∙

research

∙ 04/04/2023

Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation

ChatGPT, a large-scale language model based on the advanced GPT-3.5 arch...

0 Tao Fang, et al. ∙

research

∙ 01/26/2023

Variable Selection for Doubly Robust Causal Inference

Confounding control is crucial and yet challenging for causal inference ...

0 Eunah Cho, et al. ∙

research

∙ 01/13/2023

Efficient and robust transfer learning of optimal individualized treatment regimes with right-censored survival data

An individualized treatment regime (ITR) is a decision rule that assigns...

0 Pan Zhao, et al. ∙

research

∙ 11/03/2022

A Bayesian Semiparametric Method For Estimating Causal Quantile Effects

Standard causal inference characterizes treatment effect through average...

0 Steven G. Xu, et al. ∙

research

∙ 10/28/2022

Ultra-fast, programmable, and electronics-free soft robots enabled by snapping metacaps

Soft robots have a myriad of potentials because of their intrinsically c...

0 Lishuai Jin, et al. ∙

research

∙ 10/08/2022

Matching Estimators of Causal Effects in Clustered Observational Studies with Application to Quantifying the Impact of Marine Protected Areas on Biodiversity

Marine conservation preserves fish biodiversity, protects marine and coa...

0 Can Cui, et al. ∙

research

∙ 10/05/2022

Transporting survival of an HIV clinical trial to the external target populations

Due to the heterogeneity of the randomized controlled trial (RCT) and ex...

0 Dasom Lee, et al. ∙

research

∙ 09/26/2022

Self-supervised Denoising via Low-rank Tensor Approximated Convolutional Neural Network

Noise is ubiquitous during image acquisition. Sufficient denoising is of...

0 Chenyin Gao, et al. ∙

research

∙ 09/23/2022

Faith: An Efficient Framework for Transformer Verification on GPUs

Transformer verification draws increasing attention in machine learning ...

0 Boyuan Feng, et al. ∙

research

∙ 08/01/2022

Towards R-learner of conditional average treatment effects with a continuous treatment: T-identification, estimation, and inference

The R-learner has been popular in causal inference as a flexible and eff...

0 Yichi Zhang, et al. ∙

research

∙ 06/02/2022

Soft calibration for selection bias problems under mixed-effects models

Calibration weighting has been widely used for correcting selection bias...

0 Chenyin Gao, et al. ∙

research

∙ 05/24/2022

UMSNet: An Universal Multi-sensor Network for Human Activity Recognition

Human activity recognition (HAR) based on multimodal sensors has become ...

7 Jialiang Wang, et al. ∙

research

∙ 04/25/2022

Trials with Irregular and Informative Assessment Times: A Sensitivity Analysis Approach

Many trials are designed to collect outcomes at pre-specified times afte...

0 Bonnie Smith, et al. ∙

research

∙ 04/23/2022

When Doubly Robust Methods Meet Machine Learning for Estimating Treatment Effects from Real-World Data: A Comparative Study

Observational cohort studies are increasingly being used for comparative...

0 Xiaoqing Tan, et al. ∙

research

∙ 03/28/2022

Functional principal component analysis for longitudinal observations with sampling at random

Functional principal component analysis has been shown to be invaluable ...

0 Peijun Sang, et al. ∙

research

∙ 03/20/2022

Robust analyses for longitudinal clinical trials with missing and non-normal continuous outcomes

Missing data is unavoidable in longitudinal clinical trials, and outcome...

0 Siyi Liu, et al. ∙

research

∙ 03/17/2022

Sensitivity analysis in longitudinal clinical trials via distributional imputation

Missing data is inevitable in longitudinal clinical trials. Conventional...

0 Siyi Liu, et al. ∙

research

∙ 02/23/2022

Nearest neighbor ratio imputation with incomplete multi-nomial outcome in survey sampling

Nonresponse is a common problem in survey sampling. Appropriate treatmen...

0 Chenyin Gao, et al. ∙

research

∙ 01/17/2022

Generalizable survival analysis of randomized controlled trials with observational studies

In the presence of heterogeneity between the randomized controlled trial...

0 Dasom Lee, et al. ∙

research

∙ 01/17/2022

Targeted Optimal Treatment Regime Learning Using Summary Statistics

Personalized decision-making, aiming to derive optimal individualized tr...

0 Jianing Chu, et al. ∙

research

∙ 12/11/2021

Multiply robust estimators in longitudinal studies with missing data under control-based imputation

Longitudinal studies are often subject to missing data. The ICH E9(R1) a...

0 Siyi Liu, et al. ∙

research

∙ 08/19/2021

Transfer learning of individualized treatment rules from experimental to real-world data

Individualized treatment effect lies at the heart of precision medicine....

0 Lili Wu, et al. ∙

research

∙ 07/26/2021

Outcome-Adjusted Balance Measure for Generalized Propensity Score Model Selection

In this article, we propose the outcome-adjusted balance measure to perf...

0 Honghe Zhao, et al. ∙

research

∙ 03/07/2021

Estimating intervention effects on infectious disease control: the effect of community mobility reduction on Coronavirus spread

Understanding the effects of interventions, such as restrictions on comm...

0 Andrew Giffin, et al. ∙

research

∙ 03/05/2021

Estimation of Partially Conditional Average Treatment Effect by Hybrid Kernel-covariate Balancing

We study nonparametric estimation for the partially conditional average ...

0 Jiayi Wang, et al. ∙

research

∙ 02/27/2021

Instrumental variables, spatial confounding and interference

Unobserved spatial confounding variables are prevalent in environmental ...

0 Andrew Giffin, et al. ∙

research

∙ 12/22/2020

A spectral adjustment for spatial confounding

Adjusting for an unmeasured confounder is generally an intractable probl...

0 Yawen Guan, et al. ∙

research

∙ 12/03/2020

Multiply robust estimation of causal effects under principal ignorability

Causal inference concerns not only the average effect of the treatment o...

0 Zhichao Jiang, et al. ∙

research

∙ 11/16/2020

Causal inference methods for combining randomized trials and observational studies: a review

With increasing data availability, treatment causal effects can be evalu...

0 Bénédicte Colnet, et al. ∙

research

∙ 10/26/2020

A Distributed Training Algorithm of Generative Adversarial Networks with Quantized Gradients

Training generative adversarial networks (GAN) in a distributed fashion ...

0 Xiaojun Chen, et al. ∙

research

∙ 08/30/2020

Robust inference of conditional average treatment effects using dimension reduction

It is important to make robust inference of the conditional average trea...

0 Ming-Yueh Huang, et al. ∙

research

∙ 07/25/2020

Improved Inference for Heterogeneous Treatment Effects Using Real-World Data Subject to Hidden Confounding

The heterogeneity of treatment effect (HTE) lies at the heart of precisi...

0 Shu Yang, et al. ∙

research

∙ 07/09/2020

SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization

With the increasing popularity of graph-based learning, Graph Neural Net...

0 Boyuan Feng, et al. ∙

research

∙ 07/06/2020

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have ...

0 Brian J. Reich, et al. ∙

research

∙ 07/05/2020

SMIM: a unified framework of Survival sensitivity analysis using Multiple Imputation and Martingale

Censored survival data are common in clinical trial studies. We propose ...

0 Shu Yang, et al. ∙

research

∙ 06/30/2020

Generalized propensity score approach to causal inference with spatial interference

Many spatial phenomena exhibit treatment interference where treatments a...

0 Andrew Giffin, et al. ∙

research

∙ 05/25/2020

Spatially varying causal effect models

We establish causal effect models that allow for time- and spatially var...

0 Shu Yang, et al. ∙

research

∙ 05/21/2020

Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimation

Parallel randomized trial (RT) and real-world (RW) data are becoming inc...

0 Shu Yang, et al. ∙

research

∙ 03/12/2020

A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output

Wildland fire smoke contains hazardous levels of fine particulate matter...

0 Alexandra Larsen, et al. ∙

research

∙ 03/02/2020

Integrative analysis of randomized clinical trials with real world evidence studies

We leverage the complementing features of randomized clinical trials (RC...

0 Lin Dong, et al. ∙

research

∙ 01/16/2020

Double score matching estimators of average and quantile treatment effects

Propensity score matching has a long tradition for handling confounding ...

0 Shu Yang, et al. ∙

research

∙ 01/09/2020

Statistical Data Integration in Survey Sampling: A Review

Finite population inference is a central goal in survey sampling. Probab...

0 Shu Yang, et al. ∙

research

∙ 11/27/2019

Causal inference of hazard ratio based on propensity score matching

Propensity score matching is commonly used to draw causal inference from...

0 Shuhan Tang, et al. ∙

research

∙ 11/12/2019

A Unified Framework for Causal Inference with Multiple Imputation Using Martingale

Multiple imputation is widely used to handle confounders missing at rand...

0 Qian Guan, et al. ∙

Shu Yang

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