報(bào) 告 人：李忠華 副教授
報(bào)告題目：Efficient Quantile Covariate Adjusted Response Adaptive Experiments
報(bào)告時(shí)間：2024年12月20日（星期五）下午3:30-4:30
報(bào)告地點(diǎn)：靜遠(yuǎn)樓1506學(xué)術(shù)報(bào)告廳
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院、數(shù)學(xué)研究院、科學(xué)技術(shù)研究院
報(bào)告人簡(jiǎn)介：
李忠華，南開大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)學(xué)院副教授，曾受邀訪問美國(guó)北卡羅萊納大學(xué)教堂山分校、明尼蘇達(dá)大學(xué)等。研究方向?yàn)榻y(tǒng)計(jì)質(zhì)量控制、變點(diǎn)、高維統(tǒng)計(jì)推斷、網(wǎng)絡(luò)數(shù)據(jù)分析等。合作出版專著1本，發(fā)表學(xué)術(shù)論文50余篇。現(xiàn)任中國(guó)數(shù)學(xué)會(huì)概率統(tǒng)計(jì)分會(huì)副秘書長(zhǎng)、中國(guó)現(xiàn)場(chǎng)統(tǒng)計(jì)研究會(huì)統(tǒng)計(jì)學(xué)歷史與文化分會(huì)副理事長(zhǎng)、中國(guó)優(yōu)選法統(tǒng)籌法及經(jīng)濟(jì)數(shù)學(xué)學(xué)會(huì)工業(yè)工程分會(huì)常務(wù)理事、全國(guó)工業(yè)統(tǒng)計(jì)學(xué)教學(xué)研究會(huì)理事、國(guó)際質(zhì)量工程期刊Quality Engineering編委、美國(guó)Mathematical Reviews評(píng)論員等。
報(bào)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.