78 citations · 104 across the 15 of their papers we have counts for
17 papers
Rerandomization under Interference
Ziang Yuan, Xinran Li, Shuangning Li
Covariates are widely used in randomized experiments to improve precision. However, in the presence of interference, where outcomes may depend on the treatment assignments of other…
Overcoming the Incentive Collapse Paradox
Qichuan Yin, Ziwei Su, Shuangning Li
AI-assisted task delegation is increasingly common, yet human effort in such systems is costly and typically unobserved. Recent work by Bastani and Cachon (2025); Sambasivan et al.…
Covariate Adjustment Cannot Hurt: Treatment Effect Estimation under Interference with Low-Order Outcome Interactions
Xinyi Wang, Shuangning Li
In randomized experiments, covariates are often used to reduce variance and improve the precision of treatment effect estimates. However, in many real-world settings, interference…
Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?
Shuangning Li, Chonghuan Wang, Jingyan Wang
We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has observed that the stable unit treatment value assumption (SUT…
Optimal Targeting in Dynamic Systems
Yuchen Hu, Shuangning Li, Stefan Wager
Modern treatment targeting methods often rely on estimating a conditional average treatment effect (CATE) using machine learning tools. While effective in identifying who benefits…
Covariate Shift Corrected Conditional Randomization Test
Bowen Xu, Yiwen Huang, Chuan Hong +2
Conditional independence tests are crucial across various disciplines in determining the independence of an outcome variable from a treatment variable , conditioning on a se…
Dyadic Reinforcement Learning
Shuangning Li, Lluis Salvat Niell, Sung Won Choi +3
Mobile health aims to enhance health outcomes by delivering interventions to individuals as they go about their daily life. The involvement of care partners and social support netw…
Experimenting under Stochastic Congestion
Shuangning Li, Ramesh Johari, Xu Kuang +1
We study randomized experiments in a service system when stochastic congestion can arise from temporarily limited supply or excess demand. Such congestion gives rise to cross-unit…
Detecting Interference in A/B Testing with Increasing Allocation
Kevin Han, Shuangning Li, Jialiang Mao +1
In the past decade, the technology industry has adopted online randomized controlled experiments (a.k.a. A/B testing) to guide product development and make business decisions. In p…
Maxway CRT: Improving the Robustness of the Model-X Inference
Shuangning Li, Molei Liu
The model-X conditional randomization test (CRT) is a flexible and powerful testing procedure for the conditional independence hypothesis: X is independent of Y conditioning on Z.…
Network Interference in Micro-Randomized Trials
Shuangning Li, Stefan Wager
The micro-randomized trial (MRT) is an experimental design that can be used to develop optimal mobile health interventions. In MRTs, interventions in the form of notifications or m…
Cooperative learning for multiview analysis
Daisy Yi Ding, Shuangning Li, Balasubramanian Narasimhan +1
We propose a new method for supervised learning with multiple sets of features ("views"). The multiview problem is especially important in biology and medicine, where "-omics" data…
Deploying the Conditional Randomization Test in High Multiplicity Problems
Shuangning Li, Emmanuel J. Candès
This paper introduces the sequential CRT, which is a variable selection procedure that combines the conditional randomization test (CRT) and Selective SeqStep+. Valid p-values are…
Transfer learning in genome-wide association studies with knockoffs
Shuangning Li, Zhimei Ren, Chiara Sabatti +1
This paper presents and compares alternative transfer learning methods that can increase the power of conditional testing via knockoffs by leveraging prior information in external…
Searching for consistent associations with a multi-environment knockoff filter
Shuangning Li, Matteo Sesia, Yaniv Romano +2
This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across diverse environments, controlling the false discovery rate. The…
Average Direct and Indirect Causal Effects under Interference
Yuchen Hu, Shuangning Li, Stefan Wager
We propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference under cross-unit interference. Our definition is…
Random Graph Asymptotics for Treatment Effect Estimation under Network Interference
Shuangning Li, Stefan Wager
The network interference model for causal inference places all experimental units at the vertices of an undirected exposure graph, such that treatment assigned to one unit may affe…