應(yīng)yl7703永利官網(wǎng)概率統(tǒng)計(jì)研究所邀請(qǐng)，美國(guó)加州大學(xué)河濱分校馬舒潔教授將于2024年7月26日上午進(jìn)行學(xué)術(shù)報(bào)告，歡迎全校師生參加。

報(bào)告題目：Causal inference on quantile dose-response functions via local ReLU least squares weighting

時(shí)  間：7月26日（星期五）上午8:30

地  點(diǎn)：理工樓631報(bào)告廳

報(bào)告摘要：In this talk, I will introduce a novel local ReLU network least squares weighting method to estimate quantile dose-response functions in observational studies. Unlike the conventional inverse propensity weighting (IPW) method, we estimate the weighting function involved in the treatment effect estimator directly through local ReLU least squares optimization. The proposed method takes advantage of ReLU networks applied for the baseline covariates with increasing dimension to alleviate the dimensionality problem while retaining flexibility and local kernel smoothing for the continuous treatment to precisely estimate the quantile dose-response function and prepare for statistical inference. Our method enjoys computational convenience, scalability, and flexibility. It also improves robustness and numerical stability compared to the conventional IPW method. We show that the ReLU networks can break the notorious `curse of dimensionality' when the weighting function belongs to a newly introduced smoothness class.We also establish the convergence rate for the ReLU network estimator and the asymptotic normality of the proposed estimator for the quantile dose-response function. We further propose a multiplier bootstrap method to construct confidence bands for quantile dose-response functions. The finite sample performance of our proposed method is illustrated through simulations and a real data application.

歡迎廣大師生參加！

報(bào)告人簡(jiǎn)介

馬舒潔教授，于2011年在美國(guó)密歇根州立大學(xué)(Michigan State University)獲得統(tǒng)計(jì)學(xué)博士學(xué)位，現(xiàn)為美國(guó)加州大學(xué)河濱分校（University of California at Riverside）統(tǒng)計(jì)系教授、研究生項(xiàng)目主任。她是國(guó)際數(shù)理統(tǒng)計(jì)學(xué)會(huì)會(huì)士(IMS)和美國(guó)統(tǒng)計(jì)學(xué)會(huì)(ASA)會(huì)士(Fellow)、國(guó)際統(tǒng)計(jì)學(xué)會(huì)(ISI)推選會(huì)員(Elected Member)，現(xiàn)任Journal of the American Statistical Association, Journal of Business & Economic Statistics,Computational Statistics and Data Analysis,Journal of Machine Learning Research的副主編或編委。馬教授的主要研究領(lǐng)域?yàn)椋壕珳?zhǔn)醫(yī)療、亞組分析、因果推斷、大數(shù)據(jù)機(jī)器學(xué)習(xí)和深度學(xué)習(xí)、網(wǎng)絡(luò)分析、非參數(shù)和半?yún)?shù)推理以及高維和縱向數(shù)據(jù)分析，現(xiàn)已在Annals of Statistics, Journal of the American Statistical Association, Journal of Econometrics, Journal of Machine Learning Research等統(tǒng)計(jì)學(xué)、計(jì)量經(jīng)濟(jì)、機(jī)器學(xué)習(xí)的國(guó)際知名期刊上發(fā)表50余篇論文。

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