讲座题目：Localized Debiased Machine Learning: Efficient Estimation of Quantile Treatment Effects and Beyond

报告人：毛小介

报告时间：2021年6月4日（周五）上午10:00

报告地点：经管院A208

主办单位：350vip8888新葡的京集团数理经济与数理金融系

主持人：刘成

内容摘要：We consider the efficient estimation of a low-dimensional parameter in an estimating equation involving high-dimensional nuisances that depend on the parameter of interest.An important example is the (local) quantile treatment effect ((L)QTE) in causal inference, for which the efficient estimating equation involves as a nuisance the covariate-conditional cumulative distribution function evaluated at the quantile to be estimated. Debiased machine learning (DML) is a data-splitting approach to address the need to estimate nuisances using flexible machine learning methods that may not satisfy strong metric entropy conditions, but applying it to problems with parameter-dependent nuisances is impractical. For (L)QTE estimation, DML requires we learn the whole conditional cumulative distribution function, conditioned on potentially high-dimensional covariates, which is far more challenging than the standard supervised regression task in machine learning. We instead propose localized debiased machine learning (LDML), a new data-splitting approach that avoids this burdensome step and needs only estimate the nuisances at a single initial rough guess for the parameter.

For (L)QTE estimation, this involves just learning two binary regression (i.e., classification) models, for which many standard, time-tested machine learning methods exist, and the initial rough guess may be given by inverse propensity weighting. We prove that under lax rate conditions on nuisances, our estimator has the same favorable asymptotic behavior as the infeasible oracle estimator that solves the estimating equation with the unknown true nuisance functions. Thus, our proposed approach uniquely enables practically-feasible and theoretically-grounded efficient estimation of important quantities in causal inference such as (L)QTEs and in other coarsened data settings.

主讲人简介：毛小介即将加入清华大学管理科学与工程系任助理教授，此前获得康奈尔大学统计学博士学位与新葡的京集团数理经济学士学位。其研究领域为data-driven decision making和causal inference，有多篇论文发表于Management Science等管科顶级期刊和NeurIPS, AISTATS, COLT等机器学习顶级会议。