5 papers
Global Average Treatment Effects for Individualized Randomization Experiments with Aggregate Data
Shuguang Yu, Ting Li, Yuchen Lu +5
Individualized randomized experiments are central to online platforms for optimizing personalized decisions in complex environments. In two-sided markets, however, standard treatme…
Learning Perturbations to Extrapolate Your LLM
Zetai Cen, Chenfei Gu, Jin Zhu +3
Recent advancements in large language models demonstrate that injecting perturbations can substantially enhance extrapolation performance. However, current approaches often rely on…
Robust Sequential Experimental Design for A/B Testing
Qianglin Wen, Xiangkun Wu, Chengchun Shi +4
Experimental design has emerged as a powerful approach for improving the sample efficiency of A/B testing, yet existing designs rely critically on correctly specified models. We st…
Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning
Xiangkun Wu, Qianglin Wen, Yingying Zhang +3
A/B testing has become a gold standard for modern technological companies to conduct policy evaluation. Yet, its application to time series experiments, where policies are sequenti…
Causal Inference in Biomedical Imaging via Functional Linear Structural Equation Models
Ting Li, Ethan Fan, Tengfei Li +1
Understanding the causal effects of organ-specific features from medical imaging on clinical outcomes is essential for biomedical research and patient care. We propose a novel Func…