Design-based theory for Lasso adjustment in randomized block experiments and rerandomized experiments
報告人簡介
楊玥含，中央財經大學統計與數學學院教授，北京大學博士。中央財經大學青年英才、龍馬學者青年學者。主要從事復雜數據建模、因果推斷、遷移學習等研究，主持多項國家自然科學基金，多次獲得優秀論文獎及實踐教學獎。作為獨立作者、第一及通信作者在Journal of the American Statistical Association、Biometrika、Journal of Business and Economics Statistics、Pattern Recognition、《中國科學：數學》等國內外期刊發表論文40余篇。
內容簡介
Design-based theory for Lasso adjustment in randomized block experiments and rerandomized experiments Blocking, a special case of rerandomization, is routinely implemented in the design stage of randomized experiments to balance the baseline covariates. This study proposes a regression adjustment method based on the least absolute shrinkage and selection operator (Lasso) to efficiently estimate the average treatment effect in randomized block experiments with high-dimensional covariates. We derive the asymptotic properties of the proposed estimator and outline the conditions under which this estimator is more efficient than the unadjusted one. We provide a conservative variance estimator to facilitate valid inferences. Our framework allows one treated or control unit in some blocks and heterogeneous propensity scores across blocks, thus including paired experiments and finely stratified experiments as special cases. We further accommodate rerandomized experiments and a combination of blocking and rerandomization. Moreover, our analysis allows both the number of blocks and block sizes to tend to infinity, as well as heterogeneous treatment effects across blocks without assuming a true outcome data-generating model. Simulation studies and two real-data analyses demonstrate the advantages of the proposed method.
