Peng Ding

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Tyler J. VanderWeeleJohn L. Loeb and Frances Lehman Loeb Professor of Epidemiology, Harvard UniversityVerified email at hsph.harvard.edu
Xinran LiAssistant professor at University of ChicagoVerified email at uchicago.edu
Zhichao JiangSun Yat-sen UniversityVerified email at mail.sysu.edu.cn
Anqi ZhaoAssistant Professor, Duke UniversityVerified email at duke.edu
Shu YangNorth Carolina State UniversityVerified email at ncsu.edu
Avi FellerUC BerkeleyVerified email at berkeley.edu
Fan LiDepartment of Statistical Science, Duke UniversityVerified email at duke.edu
Tirthankar DasguptaRutgers UniversityVerified email at rutgers.edu
Jasjeet SekhonEugene Meyer Professor of Data Science, Political Science, and Statistics, Yale UniversityVerified email at yale.edu
Jiannan LuAppleVerified email at apple.com
LIHUA LEIStanford UniversityVerified email at stanford.edu
Fangzhou SuVerified email at berkeley.edu
Alexander D'AmourResearch Scientist, Google DeepMindVerified email at google.com
Fabrizia MealliUniversity of FlorenceVerified email at ds.unifi.it
Martin WainwrightProfessor, EECS/Mathematics, Massachusetts Institute of TechnologyVerified email at mit.edu
Wenlong MouUniversity of TorontoVerified email at utoronto.ca
Alessandra MatteiAssociate Professor of Statistics, Dipartimento di Statistica, Informatica, ApplicazioniVerified email at unifi.it
Dennis ShenAssistant Professor at USC Data Sciences and OperationsVerified email at marshall.usc.edu
Fan YangProfessor, Yau Mathematical Sciences Center, Tsinghua UniversityVerified email at tsinghua.edu.cn
Serena WangUC BerkeleyVerified email at berkeley.edu

Peng Ding

University of California, Berkeley

Verified email at berkeley.edu - Homepage

causal inference experimental design missing data


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Sensitivity analysis in observational research: introducing the E-value TJ VanderWeele, P Ding Annals of Internal Medicine 167 (4), 268-274, 2017	4316	2017
Web site and R package for computing E-values MB Mathur, P Ding, CA Riddell, TJ VanderWeele Epidemiology 29 (5), e45-e47, 2018	618	2018
Sensitivity analysis without assumptions P Ding, TJ VanderWeele Epidemiology (Cambridge, Mass.) 27 (3), 368, 2016	611	2016
Overlap in observational studies with high-dimensional covariates A D’Amour, P Ding, A Feller, L Lei, J Sekhon Journal of Econometrics 221 (2), 644-654, 2021	263	2021
General forms of finite population central limit theorems with applications to causal inference X Li, P Ding Journal of the American Statistical Association 112 (520), 1759-1769, 2017	233	2017
Randomization inference for treatment effect variation P Ding, A Feller, L Miratrix Journal of the Royal Statistical Society, Series B (Statistical Methodology), 2014	169	2014
Causal Inference: A Missing Data Perspective P Ding, F Li Statistical Science 33 (2), 214-237, 2018	167	2018
To adjust or not to adjust? Sensitivity analysis of M-bias and butterfly-bias P Ding, LW Miratrix Journal of Causal Inference 3 (1), 41-57, 2015	158	2015
Causal inference K Kuang, L Li, Z Geng, L Xu, K Zhang, B Liao, H Huang, P Ding, W Miao, ... Engineering 6 (3), 253-263, 2020	141	2020
Asymptotic theory of rerandomization in treatment–control experiments X Li, P Ding, DB Rubin Proceedings of the National Academy of Sciences 115 (37), 9157-9162, 2018	135	2018
Principal stratification analysis using principal scores P Ding, J Lu Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2017	132	2017
Identifiability of normal and normal mixture models with nonignorable missing data W Miao, P Ding, Z Geng Journal of the American Statistical Association 111 (516), 1673-1683, 2016	124	2016
Decomposing treatment effect variation P Ding, A Feller, L Miratrix Journal of the American Statistical Association 114 (525), 304-317, 2019	114	2019
A paradox from randomization-based causal inference P Ding Statistical Science, 2017	109	2017
On the Conditional Distribution of the Multivariate t Distribution P Ding The American Statistician 70 (3), 293-295, 2016	107	2016
Combining multiple observational data sources to estimate causal effects S Yang, P Ding Journal of the American Statistical Association 115 (531), 1540-1554, 2020	106	2020
Technical considerations in the use of the E-value TJ VanderWeele, P Ding, M Mathur Journal of Causal Inference 7 (2), 20180007, 2019	102	2019
A bracketing relationship between difference-in-differences and lagged-dependent-variable adjustment P Ding, F Li Political Analysis 27 (4), 605-615, 2019	100	2019
Identifiability and estimation of causal effects by principal stratification with outcomes truncated by death P Ding, Z Geng, W Yan, XH Zhou Journal of the American Statistical Association 106 (496), 2011	99	2011
Rerandomization and regression adjustment X Li, P Ding Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2020	98	2020

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