報 告 人：李忠華 副教授

報告題目：Efficient Quantile Covariate Adjusted Response Adaptive Experiments

報告時間：2024年12月20日（星期五）下午3:30-4:30

報告地點：靜遠樓1506學術報告廳
主辦單位：數學與統計學院、數學研究院、科學技術研究院

報告人簡介：

       李忠華，南開大學統計與數據科學學院副教授，曾受邀訪問美國北卡羅萊納大學教堂山分校、明尼蘇達大學等。研究方向為統計質量控制、變點、高維統計推斷、網絡數據分析等。合作出版專著1本，發表學術論文50余篇。現任中國數學會概率統計分會副秘書長、中國現場統計研究會統計學歷史與文化分會副理事長、中國優選法統籌法及經濟數學學會工業工程分會常務理事、全國工業統計學教學研究會理事、國際質量工程期刊Quality Engineering編委、美國Mathematical Reviews評論員等。

報告摘要：

       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.