时 间：2024年6月21日 10:00 - 11:00
地 点：普陀校区理科大楼A1414
报告人：赵安琪 杜克大学助理教授
主持人：项冬冬 华东师范大学教授
摘 要：
Covariate adjustment can improve precision in analyzing randomized experiments.With fully observed data, regression adjustment and propensity score weighting are asymptotically equivalent in improving efficiency over unadjusted analysis. When some outcomes are missing, we consider combining these two adjustment methods with inverse probability of observation weighting for handling missing outcomes, and show that the equivalence between the two methods breaks down. Regression adjustment no longer ensures efficiency gain over unadjusted analysis unless the true outcome model is linear in covariates or the outcomes are missing completely at random. Propensity score weighting, in contrast, still guarantees efficiency over unadjusted analysis, and including more covariates in adjustment never harms asymptotic efficiency. Moreover, we establish the value of using partially observed covariates to secure additional efficiency by the missingness indicator method, which imputes all missing covariates by zero and uses the union of the completed covariates and corresponding missingness indicators as the new, fully observed covariates. Based on these findings, we recommend using regression adjustment in combination with the missingness indicator method if the linear outcome model or missing complete at random assumption is plausible and using propensity score weighting with the missingness indicator method otherwise.
报告人简介：
Anqi Zhao is an Assistant Professor at the Fuqua School of Business, Duke University. She obtained her Phd in Statistics from Harvard in 2016 and BS in Statistics from Peking University in 2010. Her research interest lies in the union of causal inference and design of experiments (DOE).