Shu Yang

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Jae kwang KimLAS Dean's professor, Statistics Department, Iowa State UniversityEmail yang diverifikasi di iastate.edu
Brian J ReichProfessor, North Carolina State UniversityEmail yang diverifikasi di ncsu.edu
Peng DingUniversity of California, BerkeleyEmail yang diverifikasi di berkeley.edu
Ana RappoldStatistician, U.S. Environmental Protection AgencyEmail yang diverifikasi di epa.gov
Chenyin GaoHarvard UniversityEmail yang diverifikasi di hsph.harvard.edu
Donglin ZengProfessor of Biostatistics, University of MichiganEmail yang diverifikasi di umich.edu
Andrew GiffinStatistician, Food and Drug AdministrationEmail yang diverifikasi di fda.hhs.gov
Dasom LeeNorth Carolina State UniversityEmail yang diverifikasi di ncsu.edu
Linbo WangUniversity of TorontoEmail yang diverifikasi di utoronto.ca
Lili WuMicrosoft Research NYCEmail yang diverifikasi di microsoft.com
Julie JosseSenior Researcher Inria,Email yang diverifikasi di inria.fr
Yawen GuanColorado State UniversityEmail yang diverifikasi di colostate.edu
Ilya LipkovichEli Lilly and CompanyEmail yang diverifikasi di lilly.com
Judith LokProfessor of Mathematics and StatisticsEmail yang diverifikasi di bu.edu
guido imbensStanford UniversityEmail yang diverifikasi di stanford.edu
Lin DongData Scientist, GoogleEmail yang diverifikasi di google.com
Dehan KongUniversity of TorontoEmail yang diverifikasi di utoronto.ca
Anastasios TsiatisNorth Carolina State UniversityEmail yang diverifikasi di ncsu.edu
Rui SongNorth Carolina State UniversityEmail yang diverifikasi di ncsu.edu
Jianwen CaiCary C. Boshamer Distinguished Professor of Biostatistics, University of North Carolina-Chapel HillEmail yang diverifikasi di bios.unc.edu

Ikuti

Shu Yang

North Carolina State University

Email yang diverifikasi di ncsu.edu - Beranda

Causal inference and missing data analysis


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
Propensity score matching and subclassification in observational studies with multi‐level treatments S Yang, GW Imbens, Z Cui, DE Faries, Z Kadziola Biometrics 72 (4), 1055-1065, 2016	169	2016
Causal inference methods for combining randomized trials and observational studies: a review B Colnet, I Mayer, G Chen, A Dieng, R Li, G Varoquaux, JP Vert, J Josse, ... Statistical science 39 (1), 165-191, 2024	157	2024
Combining multiple observational data sources to estimate causal effects S Yang, P Ding Journal of the American Statistical Association, 2020	109	2020
A review of spatial causal inference methods for environmental and epidemiological applications BJ Reich, S Yang, Y Guan, AB Giffin, MJ Miller, A Rappold International Statistical Review 89 (3), 605-634, 2021	104	2021
Statistical data integration in survey sampling: A review S Yang, JK Kim Japanese Journal of Statistics and Data Science 3 (2), 625-650, 2020	102	2020
Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores S Yang, P Ding Biometrika 105 (2), 487-493, 2018	100	2018
Doubly robust inference when combining probability and non-probability samples with high dimensional data S Yang, JK Kim, R Song Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2020	83	2020
Causal inference with confounders missing not at random S Yang, L Wang, P Ding Biometrika 106 (4), 875-888, 2019	68	2019
Improving trial generalizability using observational studies D Lee, S Yang, L Dong, X Wang, D Zeng, J Cai Biometrics 79 (2), 1213-1225, 2023	65	2023
Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation S Yang, C Gao, D Zeng, X Wang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2023	61	2023
Fractional imputation in survey sampling: A comparative review S Yang, JK Kim	53	2016
Propensity score weighting for causal inference with clustered data S Yang Journal of Causal Inference 6 (2), 20170027, 2018	47	2018
Multiply robust estimation of causal effects under principal ignorability Z Jiang, S Yang, P Ding Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2022	42	2022
Factors associated with parent concern for child weight and parenting behaviors KL Peyer, G Welk, L Bailey-Davis, S Yang, JK Kim Childhood Obesity 11 (3), 269-274, 2015	41	2015
Semiparametric estimation of structural failure time models in continuous-time processes S Yang, K Pieper, F Cools Biometrika 107 (1), 123-136, 2020	39	2020
Sensitivity analysis for unmeasured confounding in coarse structural nested mean models S Yang, J Lok Statistica Sinica, doi:10.5705/ss.202016.0133, 2017	39	2017
Risks associated with discontinuation of oral anticoagulation in newly diagnosed patients with atrial fibrillation: results from the GARFIELD‐AF Registry F Cools, D Johnson, AJ Camm, JP Bassand, FWA Verheugt, S Yang, ... Journal of Thrombosis and Haemostasis 19 (9), 2322-2334, 2021	38	2021
Improved inference for heterogeneous treatment effects using real-world data subject to hidden confounding S Yang, D Zeng, X Wang arXiv preprint arXiv:2007.12922, 2020	38	2020
Integrative -learner of heterogeneous treatment effects combining experimental and observational studies L Wu, S Yang Conference on Causal Learning and Reasoning, 904-926, 2022	35	2022
A note on multiple imputation for method of moments estimation S Yang, JK Kim Biometrika 103 (1), 244-251, 2016	35	2016

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