Rui Wang 王瑞
@RuiWang97
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PhD student @UWBiostat; alumnus of @PKU1898, school of public health and national school of development. #biostatistics #economics #statistics #epidemiology
Seattle, WA
Joined December 2022
RT @mathSTb: Meixide, van der Laan: Causal inference via implied interventions https://t.co….
arxiv.org
In the context of having an instrumental variable, the standard practice in causal inference begins by targeting an effect of interest and proceeds by formulating assumptions enabling...
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RT @UWBiostat: Congratulations to Andrea Rotnitzky, professor of biostatistics @uwsph / @UWBiostat who has been named Fellow of the Institu….
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We applied our general theory to three examples in causal inference: additive structural mean model with many weak IVs, multiplicative structural mean model with many weak IVs and regression based proximal causal inference with many weak treatment proxies.
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We proposed a two-step debiasing CUE (which belongs to the GMM family) estimator for weakly identified finite-dimensional parameter, and established its asymptotic properties. Neyman orthogonality plays a stronger role under the many weak asymptotics.
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Moreover, modern causal inference methods usually involves function-valued parameters, which imposes another challenge for estimation and inference under weak identification.
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Weak proxies is also a problem in proximal causal inference. To identify causal effects using proxies, we typically require proxies to be correlated with unmeasured confounders. When the correlation is weak, substantial bias may arise.
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Weak identification is a common problem in statistics. Weak instrumental variable (IV) problem is probably the most well-known one, which has been studied extensively.
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Our recent preprint titled “GMM with many weak moment conditions and nuisance parameters: General theory and applications in causal inference” is available on Arxiv now. We give two IV examples and one proximal causal inference example in the paper.
Wang, Chan, Ye: GMM with Many Weak Moment Conditions and Nuisance Parameters: .
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🎉🎉🎉🎉🎉.
Congratulations to the following @uwsph @UWBiostat PhD students who received best paper awards and/or honorable mentions in the 2025 American Statistical Association (ASA) student paper competitions. Read more at:
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RT @triadsou: A General Form of Covariate Adjustment in Clinical Trials under Covariate-Adaptive Randomization. Marlena S Bannick, Jun Shao….
academic.oup.com
Abstract. In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatme
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Our new paper ‘Estimating controlled direct treatment effects on pain intensity using structural mean models: application to pain randomized controlled trials’ is on Medrxiv now.
medrxiv.org
Analytical methods to incorporate potential concurrent analgesic use into primary statistical summaries are underutilized in pain randomized controlled trials (RCTs). Without valid inclusion of...
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RT @mathSTb: Xu, Witten, Shojaie: A Unified Framework for Semiparametrically Efficient Semi-Supe. .
arxiv.org
We consider statistical inference under a semi-supervised setting where we have access to both a labeled dataset consisting of pairs $\{X_i, Y_i \}_{i=1}^n$ and an unlabeled dataset $\{ X_i...
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RT @UWStat: Alex Jiang receives Best Student Paper Award from the American Statistical Association! See details here: .
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I watched this video for many times. Her responses were so decent and powerful.
It is just so sad that the #NeurIPS2024 main conference ended with such a racist remark by a faculty when talking about ethics. How ironic!. I also want to commend the Chinese student who spoke up right on spot. She was respectful, decent, and courageous. Her response was
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Looks like a very useful book. I am going to read it for sure.
🚨 New Textbook on Conformal Prediction 🚨. “The goal of this book is to teach the reader about the fundamental technical arguments that arise when researching conformal prediction and related questions in distribution-free inference. Many of these
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RT @ken_rothman: Just published: the third edition of Epidemiology, An Introduction, with stellar co-authors Krista Huybrechts and Ellie Mu….
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RT @UWBiostat: Congrats to @uwsph Ting Ye! .“The methods we develop have the potential to change how omics data are analyzed, providing new….
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🎉.
A tutorial on Longitudinal Modified Treatment Policies-- a flexible method for defining, identifying, and estimating causal parameters of interest-- is now in @EpidemiologyLWW!. 🔗 cc🌟coauthors: @dasalazarb @nickWillyamz @kara_rudolph @ildiazm.
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