Hongtu Zhu (朱宏图) is a tenured professor at the University of North Carolina at Chapel Hill (UNC-CH), with appointments in the departments of Biostatistics, Statistics, Computer Science, and Genetics. Previously, he served as the DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing. He is internationally recognized for his expertise in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. Since 2011, Professor Zhu has been an elected Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. In 2019, he was honored with the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice. Over the course of his prolific career, professor Zhu has authored over 310 papers published in prestigious journals, including Nature, Science, Cell, Nature Genetics, Nature Communication, PNAS, Biometrika, JASA, AOS, and JRSSB. He has also presented 51 papers at top-tier conferences such as NeurIPS, IJCAI, AAAI, KDD, ICDM, MICCAI, and IPMI. He has mentored a total of 30 post-doctoral researchers/visiting scholars, 10 visiting PhD students, 29 graduated PhD students, and is currently advising 7 PhD students. Many of his trainees now hold tenured or tenure-track faculty positions at major universities in the US, Canada, Korea, and China.

Title: Causal Inference and Experimental Design in Two-sided Markets

Abstract: Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. Analyzing the causal relationship between platform policies and outcomes of interest is of great importance to improve key platform metrics. This study focuses on capturing dynamic treatment effects in complex temporal/spatial experiments and designing informative experiments. We propose a temporal/spatio-temporal varying coefficient decision process (VCDP) model to characterize dynamic treatment effects. Average treatment effects are decomposed into direct and indirect effects (DE and IE) with estimation and inference procedures developed for both. Meanwhile, we establish a framework for calculating conditional quantile treatment effects (CQTE) based on independent characteristics. Notably, we demonstrate that dynamic CQTE equals the sum of individual CQTEs across time under specific model assumptions. Additionally, we propose three optimal allocation strategies for sequential treatments in dynamic settings to minimize variance in treatment effect estimation. Estimation procedures based on off-policy evaluation (OPE) methods are developed. Theoretical properties of the proposed methods are established, including weak convergence, asymptotic power, and optimality of the proposed treatment allocation design. Extensive simulations and real data analyses support the usefulness of the proposed methods.