報告題目：New Estimation Procedures of Conditional Average Treatment Effects
報告人：童行偉 教授 北京師範大學
報告時間：2021年6月11日 10：00-11：00
報告地點：騰訊會議 會議 ID：780 619 962 會議密碼：0611
校内聯系人：王培潔 wangpeijie@jlu.edu.cn
報告摘要：Conditional average treatment effect (CATE) is designed to capture the heterogeneity of treatment effect across subpopulations. In this paper, we propose a new nonparametric estimation strategy for CATE based on the propensity score and projection theory. The proposed approach has two advantages over the existing ones. First, it does not need to estimate the two nonparametric regression functions of the outcome on many covariates for treated and control groups, and obtain their predicted values via extrapolation. Second, the proposed method includes propensity score as a new covariate in nonparametric regression model, thus it can effectively overcome the hazardous impact due to extreme weights (propensity score close to 0 or 1) in weighting estimators. Meanwhile, the proposed procedure does not rely on outcome model specication. We establish the consistency of the proposed estimator, and further show that it asymptotically follows an normal distribution and the associated variance can be estimated. Simulation studies indicate that the proposed procedures outperform competing ones. We further illustrate the proposed procedures by an empirical analysis of a real-world dataset.
報告人簡介：童行偉，北京師範大學統計學院教授，博士生導師。主要從事生物統計，金融統計等方向的研究。中國現場統計研究會常務理事，概率統計學會常務理事,《應用概率統計》雜志編委；主持一項科技部重點研發計劃子課題，主持國家自然基金面上項目3項，教育部重大科研項目1項，目前發表學術論文近50篇。
