5 citations · 8 across the 7 of their papers we have counts for
7 papers
HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
Xinrui Ruan, Zhenyu Zhao, Waverly Wei +4
Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity. Orga…
Can Language Models Boost the Power of Randomized Experiments Without Statistical Bias?
Xinrui Ruan, Xinwei Ma, Yingfei Wang +2
Randomized controlled trials (RCTs) are widely adopted for causal inference, yet cost and sample-size constraints limit power. We introduce CALM (Causal Analysis leveraging Languag…
Covariate-Adjusted Response-Adaptive Design with Delayed Outcomes
Xinwei Ma, Jingshen Wang, Waverly Wei
Covariate-adjusted response-adaptive (CARA) designs have gained widespread adoption for their clear benefits in enhancing experimental efficiency and participant welfare. These des…
Adaptive Experiments Toward Learning Treatment Effect Heterogeneity
Waverly Wei, Xinwei Ma, Jingshen Wang
Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted tre…
Fair Adaptive Experiments
Waverly Wei, Xinwei Ma, Jingshen Wang
Randomized experiments have been the gold standard for assessing the effectiveness of a treatment or policy. The classical complete randomization approach assigns treatments based…
Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups
Waverly Wei, Maya Petersen, Mark J van der Laan +3
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogenei…
Inference on the Best Policies with Many Covariates
Waverly Wei, Yuqing Zhou, Zeyu Zheng +1
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 dis…