Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Wang, Jingshen"'
Mediation analysis is a powerful tool for studying causal pathways between exposure, mediator, and outcome variables of interest. While classical mediation analysis using observational data often requires strong and sometimes unrealistic assumptions,
Externí odkaz:
http://arxiv.org/abs/2312.10563
Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in th
Externí odkaz:
http://arxiv.org/abs/2312.06883
Randomized experiments have been the gold standard for assessing the effectiveness of a treatment or policy. The classical complete randomization approach assigns treatments based on a prespecified probability and may lead to inefficient use of data.
Externí odkaz:
http://arxiv.org/abs/2310.16290
In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are kn
Externí odkaz:
http://arxiv.org/abs/2309.04957
Multi armed bandit (MAB) algorithms have been increasingly used to complement or integrate with A/B tests and randomized clinical trials in e-commerce, healthcare, and policymaking. Recent developments incorporate possible delayed feedback. While exi
Externí odkaz:
http://arxiv.org/abs/2307.00752
Developments in genome-wide association studies and the increasing availability of summary genetic association data have made the application of two-sample Mendelian Randomization (MR) with summary data increasingly popular. Conventional two-sample M
Externí odkaz:
http://arxiv.org/abs/2302.10470
Ever since the seminal work of R. A. Fisher and F. Yates, factorial designs have been an important experimental tool to simultaneously estimate the effects of multiple treatment factors. In factorial designs, the number of treatment combinations grow
Externí odkaz:
http://arxiv.org/abs/2301.12045
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgrou
Externí odkaz:
http://arxiv.org/abs/2211.14671
Modern longitudinal data, for example from wearable devices, measures biological signals on a fixed set of participants at a diverging number of time points. Traditional statistical methods are not equipped to handle the computational burden of repea
Externí odkaz:
http://arxiv.org/abs/2208.02890
Understanding the impact of the most effective policies or treatments on a response variable of interest is desirable in many empirical works in economics, statistics and other disciplines. Due to the widespread winner's curse phenomenon, conventiona
Externí odkaz:
http://arxiv.org/abs/2206.11868