报 告 人：李忠华 副教授

报告题目：Efficient Quantile Covariate Adjusted Response Adaptive Experiments

报告时间：2024年12月20日（星期五）下午3:30-4:30

报告地点：静远楼1506学术报告厅
主办单位：数学与统计学院、数学研究院、科学技术研究院

报告人简介：

       李忠华，南开大学统计与数据科学学院副教授，曾受邀访问美国北卡罗莱纳大学教堂山分校、明尼苏达大学等。研究方向为统计质量控制、变点、高维统计推断、网络数据分析等。合作出版专著1本，发表学术论文50余篇。现任中国数学会概率统计分会副秘书长、中国现场统计研究会统计学历史与文化分会副理事长、中国优选法统筹法及经济数学学会工业工程分会常务理事、全国工业统计学教学研究会理事、国际质量工程期刊Quality Engineering编委、美国Mathematical Reviews评论员等。

报告摘要：

       In program evaluation studies, understanding the heterogeneous distributional impacts of a program beyond the average effect is crucial. Quantile treatment effect (QTE) provides a natural measure to capture such heterogeneity. While much of the existing work for estimating QTE has focused on analyzing observational data based on untestable causal assumptions, little work has gone into designing randomized experiments specifically for estimating QTE. In this talk, we propose two covariate-adjusted response adaptive design strategies--fully adaptive designs and multi-stage designs--to efficiently estimate the QTE. We demonstrate that the QTE estimator obtained from our designs attains the optimal variance lower bound from a semiparametric theory perspective, which does not impose any parametric assumptions on underlying data distributions. Moreover, we show that using continuous covariates in multi-stage designs can improve the precision of the estimated QTE compared to the classical fully adaptive setting. We illustrate the finite-sample performance of our designs through Monte Carlo experiments and one synthetic case study on charitable giving. Our proposed designs offer a new approach to conducting randomized experiments to estimate QTE, which can have important implications for policy and program evaluation.