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381 results on '"Han,Shichao"'

1. MultiObjMatch: Matching with Optimal Tradeoffs between Multiple Objectives in R

Author

Han, Shichao and Pimentel, Samuel D.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

In an observational study, matching aims to create many small sets of similar treated and control units from initial samples that may differ substantially in order to permit more credible causal inferences. The problem of constructing matched sets may be formulated as an optimization problem, but it can be challenging to specify a single objective function that adequately captures all the design considerations at work. One solution, proposed by \citet{pimentel2019optimal} is to explore a family of matched designs that are Pareto optimal for multiple objective functions. We present an R package, \href{https://github.com/ShichaoHan/MultiObjMatch}{\texttt{MultiObjMatch}}, that implements this multi-objective matching strategy using a network flow algorithm for several common design goals: marginal balance on important covariates, size of the matched sample, and average within-pair multivariate distances. We demonstrate the package's flexibility in exploring user-defined tradeoffs of interest via two case studies, a reanalysis of the canonical National Supported Work dataset and a novel analysis of a clinical dataset to estimate the impact of diabetic kidney disease on hospitalization costs.

Published
2024

2. Estimating Treatment Effects under Recommender Interference: A Structured Neural Networks Approach

Author

Zhan, Ruohan, Han, Shichao, Hu, Yuchen, and Jiang, Zhenling

Subjects
Economics - Econometrics, Computer Science - Machine Learning, Statistics - Methodology
Abstract

Recommender systems are essential for content-sharing platforms by curating personalized content. To evaluate updates to recommender systems targeting content creators, platforms frequently rely on creator-side randomized experiments. The treatment effect measures the change in outcomes when a new algorithm is implemented compared to the status quo. We show that the standard difference-in-means estimator can lead to biased estimates due to recommender interference that arises when treated and control creators compete for exposure. We propose a "recommender choice model" that describes which item gets exposed from a pool containing both treated and control items. By combining a structural choice model with neural networks, this framework directly models the interference pathway while accounting for rich viewer-content heterogeneity. We construct a debiased estimator of the treatment effect and prove it is $\sqrt n$-consistent and asymptotically normal with potentially correlated samples. We validate our estimator's empirical performance with a field experiment on Weixin short-video platform. In addition to the standard creator-side experiment, we conduct a costly double-sided randomization design to obtain a benchmark estimate free from interference bias. We show that the proposed estimator yields results comparable to the benchmark, whereas the standard difference-in-means estimator can exhibit significant bias and even produce reversed signs.

Published
2024

18. Treatment Effect Detection with Controlled FDR under Dependence for Large-Scale Experiments

Author

Bao, Yihan, Han, Shichao, and Wang, Yong

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Online controlled experiments (also known as A/B Testing) have been viewed as a golden standard for large data-driven companies since the last few decades. The most common A/B testing framework adopted by many companies use "average treatment effect" (ATE) as statistics. However, it remains a difficult problem for companies to improve the power of detecting ATE while controlling "false discovery rate" (FDR) at a predetermined level. One of the most popular FDR-control algorithms is BH method, but BH method is only known to control FDR under restrictive positive dependence assumptions with a conservative bound. In this paper, we propose statistical methods that can systematically and accurately identify ATE, and demonstrate how they can work robustly with controlled low FDR but a higher power using both simulation and real-world experimentation data. Moreover, we discuss the scalability problem in detail and offer comparison of our paradigm to other more recent FDR control methods, e.g., knockoff, AdaPT procedure, etc., Comment: 9 pages, 5 figures

Published
2021

38. Intensive Surveillance of Porcine‐Rhesus Kidney Xenotransplant Using Different Ultrasound Techniques.

Author

Qi, Ruochen, Ma, Shuaijun, Han, Shichao, Wang, Guohui, Zhang, Xiaoyan, Liu, Kepu, Sun, Yuanyuan, Gong, Xue, Yu, Ming, Zhang, Xuan, Yang, Xiaojian, Dou, Kefeng, and Qin, Weijun

Subjects
DOPPLER ultrasonography, SHEAR waves, KIDNEY transplantation, XENOTRANSPLANTATION, HOMOGRAFTS
Abstract

Background: Significant progress has been made in kidney xenotransplantation in the past few years, and this field is accelerating towards clinical translation. Therefore, surveillance of the xenograft with appropriate tools is of great importance. Ultrasonography has been widely used in kidney allotransplantation and served as an economical and non‐invasive method to monitor the allograft. However, questions remain whether the ultrasonographic criteria established for human kidney allograft could also be applied in xenotransplantation. Methods: In the current study, we established a porcine‐rhesus life sustaining kidney xenotransplantation model. The xenograft underwent intensive surveillance using gray‐scale, colorful Doppler ultrasound as well as 2D shear wave elastography. The kidney growth, blood perfusion, and cortical stiffness were measured twice a day. These parameters were compared with the clinical data including urine output, chemistry, and pathological findings. Results: The observation continued for 16 days after transplantation. Decline of urine output and elevated serum creatinine were observed on POD9 and biopsy proven antibody‐mediated rejection was seen on the same day. The xenograft underwent substantial growth, with the long axis length increased by 32% and the volume increased by threefold at the end of observation. The resistive index of the xenograft arteries elevated in response to rejection, together with impaired cortical perfusion, while the peak systolic velocity (PSV) was not compromised. The cortical stiffness also increased along with rejection. Conclusion: In summary, the ultrasound findings of kidney xenograft shared similarities with those in allograft but possessed some unique features. A modified criteria needs to be established for further application of ultrasound in kidney xenotransplantation. [ABSTRACT FROM AUTHOR]

Published
2024
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