Design-based theory for Lasso adjustment in randomized block experiments and rerandomized experiments
报告人简介
杨玥含，中央财经大学统计与数学学院教授，北京大学博士。中央财经大学青年英才、龙马学者青年学者。主要从事复杂数据建模、因果推断、迁移学习等研究，主持多项国家自然科学基金，多次获得优秀论文奖及实践教学奖。作为独立作者、第一及通信作者在Journal of the American Statistical Association、Biometrika、Journal of Business and Economics Statistics、Pattern Recognition、《中国科学：数学》等国内外期刊发表论文40余篇。
内容简介
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.
