5 papers · 1 filter
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…
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…
SLOACI: Surrogate-Leveraged Online Adaptive Causal Inference
Yingying Fan, Zihan Wang, Waverly Wei
Adaptive experimental designs have gained increasing attention across a range of domains. In this paper, we propose a new methodological framework, surrogate-leveraged online adapt…
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…
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…